Carga de la BD CBIS-DDSM¶

In [1]:
from skimage.metrics import peak_signal_noise_ratio as psnr
import matplotlib.pyplot as plt
import cv2
import os
import numpy as np
import pandas as pd
import kagglehub
from ultralytics import YOLO
/home/mperpinav/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
In [3]:
# Nos traemos la BD CBIS-DDSM

path_cbis_ddsm = kagglehub.dataset_download("awsaf49/cbis-ddsm-breast-cancer-image-dataset")

print("Path to dataset files:", path_cbis_ddsm)
Warning: Looks like you're using an outdated `kagglehub` version (installed: 0.3.11), please consider upgrading to the latest version (0.3.12).
Path to dataset files: /home/mperpinav/.cache/kagglehub/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset/versions/1

La carga la he hecho siguiendo el notebook https://www.kaggle.com/code/gijendrap/breast-cancer-detection/notebook

In [4]:
# Cargamos los csv
dicom_data = pd.read_csv(os.path.join(path_cbis_ddsm, 'csv/dicom_info.csv'))
image_dir = os.path.join(path_cbis_ddsm, 'jpeg')
dicom_data.head()
Out[4]:
file_path image_path AccessionNumber BitsAllocated BitsStored BodyPartExamined Columns ContentDate ContentTime ConversionType ... SecondaryCaptureDeviceManufacturerModelName SeriesDescription SeriesInstanceUID SeriesNumber SmallestImagePixelValue SpecificCharacterSet StudyDate StudyID StudyInstanceUID StudyTime
0 CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.12930... CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.129308... NaN 16 16 BREAST 351 20160426 131732.685 WSD ... MATLAB cropped images 1.3.6.1.4.1.9590.100.1.2.129308726812851964007... 1 23078 ISO_IR 100 20160720.0 DDSM 1.3.6.1.4.1.9590.100.1.2.271867287611061855725... 214951.0
1 CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.24838... CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.248386... NaN 16 16 BREAST 3526 20160426 143829.101 WSD ... MATLAB full mammogram images 1.3.6.1.4.1.9590.100.1.2.248386742010678582309... 1 0 ISO_IR 100 20160720.0 DDSM 1.3.6.1.4.1.9590.100.1.2.161516517311681906612... 193426.0
2 CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.26721... CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.267213... NaN 16 16 BREAST 1546 20160503 111956.298 WSD ... MATLAB full mammogram images 1.3.6.1.4.1.9590.100.1.2.267213171011171858918... 1 0 ISO_IR 100 20160807.0 DDSM 1.3.6.1.4.1.9590.100.1.2.291043622711253836701... 161814.0
3 CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.38118... CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.381187... NaN 16 16 BREAST 97 20160503 115347.770 WSD ... MATLAB cropped images 1.3.6.1.4.1.9590.100.1.2.381187369611524586537... 1 32298 ISO_IR 100 20170829.0 DDSM 1.3.6.1.4.1.9590.100.1.2.335006093711888937440... 180109.0
4 CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.38118... CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.381187... NaN 8 8 Left Breast 3104 20160503 115347.770 WSD ... MATLAB NaN 1.3.6.1.4.1.9590.100.1.2.381187369611524586537... 1 0 ISO_IR 100 NaN DDSM 1.3.6.1.4.1.9590.100.1.2.335006093711888937440... NaN

5 rows × 38 columns

In [5]:
dicom_data.SeriesDescription.value_counts()
Out[5]:
SeriesDescription
cropped images           3567
ROI mask images          3247
full mammogram images    2857
Name: count, dtype: int64
In [6]:
cropped_images = dicom_data[dicom_data.SeriesDescription=="cropped images"].image_path
cropped_images.head()
Out[6]:
0     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.129308...
3     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.381187...
6     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.153339...
7     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.178994...
10    CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.411833...
Name: image_path, dtype: object
In [7]:
cropped_images = cropped_images.apply(lambda x: x.replace('CBIS-DDSM/jpeg', image_dir))
cropped_images.head()
Out[7]:
0     /home/mperpinav/.cache/kagglehub/datasets/awsa...
3     /home/mperpinav/.cache/kagglehub/datasets/awsa...
6     /home/mperpinav/.cache/kagglehub/datasets/awsa...
7     /home/mperpinav/.cache/kagglehub/datasets/awsa...
10    /home/mperpinav/.cache/kagglehub/datasets/awsa...
Name: image_path, dtype: object
In [8]:
full_mammogram_images = dicom_data[dicom_data.SeriesDescription == 'full mammogram images'].image_path
full_mammogram_images.head()
Out[8]:
1     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.248386...
2     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.267213...
11    CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.210396...
12    CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.749566...
15    CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.987658...
Name: image_path, dtype: object
In [9]:
full_mammogram_images = full_mammogram_images.apply(lambda x: x.replace('CBIS-DDSM/jpeg', image_dir))
full_mammogram_images.head()
Out[9]:
1     /home/mperpinav/.cache/kagglehub/datasets/awsa...
2     /home/mperpinav/.cache/kagglehub/datasets/awsa...
11    /home/mperpinav/.cache/kagglehub/datasets/awsa...
12    /home/mperpinav/.cache/kagglehub/datasets/awsa...
15    /home/mperpinav/.cache/kagglehub/datasets/awsa...
Name: image_path, dtype: object
In [10]:
ROI_mask_images = dicom_data[dicom_data.SeriesDescription == 'ROI mask images'].image_path
ROI_mask_images.head()
Out[10]:
5     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.153339...
8     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.178994...
9     CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.411833...
14    CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.236373...
20    CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.357008...
Name: image_path, dtype: object
In [11]:
ROI_mask_images = ROI_mask_images.apply(lambda x: x.replace('CBIS-DDSM/jpeg', image_dir))
ROI_mask_images.head()
Out[11]:
5     /home/mperpinav/.cache/kagglehub/datasets/awsa...
8     /home/mperpinav/.cache/kagglehub/datasets/awsa...
9     /home/mperpinav/.cache/kagglehub/datasets/awsa...
14    /home/mperpinav/.cache/kagglehub/datasets/awsa...
20    /home/mperpinav/.cache/kagglehub/datasets/awsa...
Name: image_path, dtype: object
In [12]:
# Creamos una función para corregir el path de las imagenes
def replace_path(sample, old_path, new_path):
    return sample.replace(old_path,new_path,regex=True)
In [13]:
from PIL import Image
def plot_smaples(sample, row=15, col=15):
    plt.figure(figsize=(row, col))
    for i, file in enumerate(sample[0:5]):
        cropped_images_show = Image.open(file)
        gray_img= cropped_images_show.convert("L")
        plt.subplot(1,5,i+1)
        plt.imshow(gray_img, cmap='gray')
        plt.axis('off')
    plt.show()
In [14]:
plot_smaples(cropped_images, 15,15)
In [15]:
plot_smaples(full_mammogram_images,15,15)
In [16]:
plot_smaples(ROI_mask_images)
In [17]:
def get_image_file_name(data, new_dict):
    """
    /home
    /mperpinav
    /.cache
    /kagglehub
    /datasets
    /awsaf49
    /cbis-ddsm-breast-cancer-image-dataset
    /versions
    /1
    /jpeg
    /1.3.6.1.4.1.9590.100.1.2.153339052913121382622526066491844156138/2-270.jpg
    
    return path at index [12] after split depends on split('\')
    """
    for dicom in data:
        key = dicom.split('/')[11]
        new_dict[key] = dicom
    print(f"the length of dataset ==> {len(new_dict.keys())}")
In [18]:
cropped_images_dict = dict()
full_mammo_dict = dict()
roi_img_dict = dict()

get_image_file_name(cropped_images, cropped_images_dict)
get_image_file_name(full_mammogram_images, full_mammo_dict)
get_image_file_name(ROI_mask_images, roi_img_dict)
the length of dataset ==> 3567
the length of dataset ==> 2857
the length of dataset ==> 3247
In [19]:
next(iter((cropped_images.items())))
Out[19]:
(0,
 '/home/mperpinav/.cache/kagglehub/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset/versions/1/jpeg/1.3.6.1.4.1.9590.100.1.2.129308726812851964007517874181459556304/1-172.jpg')
In [20]:
def fix_image_path(data):
    """Correct dicom paths to correct image paths."""
    for indx, image in enumerate(data.values):
#         print(f"Image Path: {image[11]}")

        img_name = image[11].split('/')[2]
#         print(f"Looking for key: {img_name}")  # Debugging step

        if img_name in full_mammo_dict:
            data.iloc[indx, 11] = full_mammo_dict[img_name]
        else:
            data.iloc[indx, 11] = None
#             print(f"KeyError: '{img_name}' not found in full_mammo_dict")  # Debugging step
        
        img_name = image[12].split('/')[2]
        if img_name in cropped_images_dict:
            data.iloc[indx, 12] = cropped_images_dict[img_name]
        else:
            data.iloc[indx, 11] = None
            # print(f"KeyError: '{img_name}' not found in cropped_images_dict")  # Debugging step

        img_name = image[13].split('/')[2]
        if img_name in roi_img_dict:
            data.iloc[indx, 13] = roi_img_dict[img_name]

        else:
            data.iloc[indx, 13] = None
In [21]:
# Cargamos los csv
mass_train = pd.read_csv(os.path.join(path_cbis_ddsm, 'csv/mass_case_description_train_set.csv'))
mass_test = pd.read_csv(os.path.join(path_cbis_ddsm, 'csv/mass_case_description_test_set.csv'))

mass_train.head()
Out[21]:
patient_id breast_density left or right breast image view abnormality id abnormality type mass shape mass margins assessment pathology subtlety image file path cropped image file path ROI mask file path
0 P_00001 3 LEFT CC 1 mass IRREGULAR-ARCHITECTURAL_DISTORTION SPICULATED 4 MALIGNANT 4 Mass-Training_P_00001_LEFT_CC/1.3.6.1.4.1.9590... Mass-Training_P_00001_LEFT_CC_1/1.3.6.1.4.1.95... Mass-Training_P_00001_LEFT_CC_1/1.3.6.1.4.1.95...
1 P_00001 3 LEFT MLO 1 mass IRREGULAR-ARCHITECTURAL_DISTORTION SPICULATED 4 MALIGNANT 4 Mass-Training_P_00001_LEFT_MLO/1.3.6.1.4.1.959... Mass-Training_P_00001_LEFT_MLO_1/1.3.6.1.4.1.9... Mass-Training_P_00001_LEFT_MLO_1/1.3.6.1.4.1.9...
2 P_00004 3 LEFT CC 1 mass ARCHITECTURAL_DISTORTION ILL_DEFINED 4 BENIGN 3 Mass-Training_P_00004_LEFT_CC/1.3.6.1.4.1.9590... Mass-Training_P_00004_LEFT_CC_1/1.3.6.1.4.1.95... Mass-Training_P_00004_LEFT_CC_1/1.3.6.1.4.1.95...
3 P_00004 3 LEFT MLO 1 mass ARCHITECTURAL_DISTORTION ILL_DEFINED 4 BENIGN 3 Mass-Training_P_00004_LEFT_MLO/1.3.6.1.4.1.959... Mass-Training_P_00004_LEFT_MLO_1/1.3.6.1.4.1.9... Mass-Training_P_00004_LEFT_MLO_1/1.3.6.1.4.1.9...
4 P_00004 3 RIGHT MLO 1 mass OVAL CIRCUMSCRIBED 4 BENIGN 5 Mass-Training_P_00004_RIGHT_MLO/1.3.6.1.4.1.95... Mass-Training_P_00004_RIGHT_MLO_1/1.3.6.1.4.1.... Mass-Training_P_00004_RIGHT_MLO_1/1.3.6.1.4.1....
In [22]:
mass_train.iloc[:, 11].head()
Out[22]:
0    Mass-Training_P_00001_LEFT_CC/1.3.6.1.4.1.9590...
1    Mass-Training_P_00001_LEFT_MLO/1.3.6.1.4.1.959...
2    Mass-Training_P_00004_LEFT_CC/1.3.6.1.4.1.9590...
3    Mass-Training_P_00004_LEFT_MLO/1.3.6.1.4.1.959...
4    Mass-Training_P_00004_RIGHT_MLO/1.3.6.1.4.1.95...
Name: image file path, dtype: object
In [23]:
mass_test.iloc[:, 11].head()
Out[23]:
0    Mass-Test_P_00016_LEFT_CC/1.3.6.1.4.1.9590.100...
1    Mass-Test_P_00016_LEFT_MLO/1.3.6.1.4.1.9590.10...
2    Mass-Test_P_00017_LEFT_CC/1.3.6.1.4.1.9590.100...
3    Mass-Test_P_00017_LEFT_MLO/1.3.6.1.4.1.9590.10...
4    Mass-Test_P_00032_RIGHT_CC/1.3.6.1.4.1.9590.10...
Name: image file path, dtype: object
In [24]:
fix_image_path(mass_train)
In [25]:
mass_train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1318 entries, 0 to 1317
Data columns (total 14 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   patient_id               1318 non-null   object
 1   breast_density           1318 non-null   int64 
 2   left or right breast     1318 non-null   object
 3   image view               1318 non-null   object
 4   abnormality id           1318 non-null   int64 
 5   abnormality type         1318 non-null   object
 6   mass shape               1314 non-null   object
 7   mass margins             1275 non-null   object
 8   assessment               1318 non-null   int64 
 9   pathology                1318 non-null   object
 10  subtlety                 1318 non-null   int64 
 11  image file path          1318 non-null   object
 12  cropped image file path  1318 non-null   object
 13  ROI mask file path       1318 non-null   object
dtypes: int64(4), object(10)
memory usage: 144.3+ KB
In [26]:
mass_train = mass_train.rename(columns={'left or right breast': 'left_or_right_breast',
                                        'image view': 'image_view',
                                        'abnormality id': 'abnormality_id',
                                        'abnormality type': 'abnormality_type',
                                        'mass shape': 'mass_shape',
                                        'mass margins': 'mass_margins',
                                        'image file path': 'image_file_path',
                                        'cropped image file path': 'cropped_image_file_path',
                                        'ROI mask file path': 'ROI_mask_file_path'})
mass_train.head(5)
Out[26]:
patient_id breast_density left_or_right_breast image_view abnormality_id abnormality_type mass_shape mass_margins assessment pathology subtlety image_file_path cropped_image_file_path ROI_mask_file_path
0 P_00001 3 LEFT CC 1 mass IRREGULAR-ARCHITECTURAL_DISTORTION SPICULATED 4 MALIGNANT 4 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
1 P_00001 3 LEFT MLO 1 mass IRREGULAR-ARCHITECTURAL_DISTORTION SPICULATED 4 MALIGNANT 4 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
2 P_00004 3 LEFT CC 1 mass ARCHITECTURAL_DISTORTION ILL_DEFINED 4 BENIGN 3 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
3 P_00004 3 LEFT MLO 1 mass ARCHITECTURAL_DISTORTION ILL_DEFINED 4 BENIGN 3 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
4 P_00004 3 RIGHT MLO 1 mass OVAL CIRCUMSCRIBED 4 BENIGN 5 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
In [27]:
fix_image_path(mass_test)
In [28]:
mass_test = mass_test.rename(columns={'left or right breast': 'left_or_right_breast',
                                        'image view': 'image_view',
                                        'abnormality id': 'abnormality_id',
                                        'abnormality type': 'abnormality_type',
                                        'mass shape': 'mass_shape',
                                        'mass margins': 'mass_margins',
                                        'image file path': 'image_file_path',
                                        'cropped image file path': 'cropped_image_file_path',
                                        'ROI mask file path': 'ROI_mask_file_path'})
mass_test.head(5)
Out[28]:
patient_id breast_density left_or_right_breast image_view abnormality_id abnormality_type mass_shape mass_margins assessment pathology subtlety image_file_path cropped_image_file_path ROI_mask_file_path
0 P_00016 4 LEFT CC 1 mass IRREGULAR SPICULATED 5 MALIGNANT 5 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
1 P_00016 4 LEFT MLO 1 mass IRREGULAR SPICULATED 5 MALIGNANT 5 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
2 P_00017 2 LEFT CC 1 mass ROUND CIRCUMSCRIBED 4 MALIGNANT 4 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
3 P_00017 2 LEFT MLO 1 mass ROUND ILL_DEFINED 4 MALIGNANT 4 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
4 P_00032 3 RIGHT CC 1 mass ROUND OBSCURED 0 BENIGN 2 /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa... /home/mperpinav/.cache/kagglehub/datasets/awsa...
In [29]:
print(f'Shape of mass_train: {mass_train.shape}')
print(f'Shape of mass_test: {mass_test.shape}')
Shape of mass_train: (1318, 14)
Shape of mass_test: (378, 14)
In [30]:
def display_images(dataset, column, number):
    """Displays images in dataset, handling missing files and converting formats."""
    
    # create figure and axes
    fig, axes = plt.subplots(1, number, figsize=(15, 5))
    
    # Loop through rows and display images
    for index, (i, row) in enumerate(dataset.head(number).iterrows()):
        image_path = row[column]
        
       # Check if image_path is valid (not None) and exists
        if image_path is None or not os.path.exists(image_path):
            # print(f"File not found or invalid path: {image_path}")
            continue
        
        image = cv2.imread(image_path)
        
        # Handle case when image can't be read
        if image is None:
            # print(f"Error reading image: {image_path}")
            continue
        
        # Convert BGR to RGB if needed (for correct color display)
        if len(image.shape) == 3 and image.shape[2] == 3:
            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        
        ax = axes[index]
        ax.imshow(image, cmap='gray' if len(image.shape) == 2 else None)
        ax.set_title(f"{row['pathology']}")
        ax.axis('off')
        print(np.array(image).shape)
    
    plt.tight_layout()
    plt.show()
In [31]:
# Se muestran algunas imagenes de ejemplo
print('Full Mammograms:\n')
display_images(mass_train, 'image_file_path', 5)
print('Cropped Mammograms:\n')
display_images(mass_train, 'cropped_image_file_path', 5)
print('ROI_mask:\n')
display_images(mass_train, 'ROI_mask_file_path', 5)
Full Mammograms:

(4808, 3024, 3)
(4800, 2656, 3)
(5491, 2986, 3)
(5491, 3046, 3)
(5491, 2911, 3)
Cropped Mammograms:

(515, 431, 3)
(292, 256, 3)
(466, 423, 3)
(427, 422, 3)
(399, 465, 3)
ROI_mask:

(4808, 3024, 3)
(4800, 2656, 3)
(5491, 2986, 3)
(5491, 3046, 3)
(5491, 2911, 3)
In [32]:
# Ahora del conjunto test
print('Full Mammograms:\n')
display_images(mass_test, 'image_file_path', 5)
print('Cropped Mammograms:\n')
display_images(mass_test, 'cropped_image_file_path', 5)
print('ROI_mask:\n')
display_images(mass_test, 'ROI_mask_file_path', 5)
Full Mammograms:

(4006, 1846, 3)
(5491, 2011, 3)
(5904, 3200, 3)
(5952, 3352, 3)
(5116, 2641, 3)
Cropped Mammograms:

(384, 385, 3)
(369, 328, 3)
(214, 195, 3)
(220, 225, 3)
(405, 376, 3)
ROI_mask:

(4006, 1846, 3)
(5491, 2011, 3)
(5904, 3200, 3)
(5952, 3352, 3)
(5116, 2641, 3)
In [33]:
# Para no hacer el preprocesamiento dos veces, unimos mass_train y mass_test
mass_data = pd.concat([mass_train, mass_test], axis=0)
In [34]:
# Mostramos como ha quedado la distribución 
fig,ax = plt.subplots()
fig.suptitle("Distribución clases conjunto mass")
num_mass = mass_data['pathology'].value_counts()
ax.bar( num_mass.index, num_mass, color = "#027a6d")
plt.show()
In [35]:
print("\nConteo mass:")
print(num_mass)
Conteo mass:
pathology
MALIGNANT                  784
BENIGN                     771
BENIGN_WITHOUT_CALLBACK    141
Name: count, dtype: int64
In [36]:
# Se juntan las clases BENIGN y BENIGN_WITHOUT_CALLBACK para tener una distribución binaria.
# Unificacion de las clases BENIGN y BENIGN_WITHOUT_CALLBACK como BENIGN
mass_data['pathology'] = mass_data['pathology'].replace('BENIGN_WITHOUT_CALLBACK', 'BENIGN')
In [37]:
print("\nConteo mass:")
num_mass = mass_data['pathology'].value_counts()
print(num_mass)
Conteo mass:
pathology
BENIGN       912
MALIGNANT    784
Name: count, dtype: int64
In [49]:
# Mostramos como ha quedado la distribución 
fig,ax = plt.subplots()
fig.suptitle("Distribución clases conjunto mass")
ax.bar( num_mass.index, num_mass, color = "#027a6d")
plt.show()

Se obseva un sesgo de clases, lo tendremos que tratar durante el entrenamiento del modelo.

PREPROCESAMIENTO¶

In [50]:
# Guardamos los tipos de anomalia que tenemos
anomalia_tipes = []
for i in mass_data['pathology'].unique():
  anomalia_tipes.append(i)
print(anomalia_tipes)
['MALIGNANT', 'BENIGN']

Escala de grises¶

Empezamos comprobando que todas las imágenes están en escala de grises

In [51]:
mass_data.columns
Out[51]:
Index(['patient_id', 'breast_density', 'left_or_right_breast', 'image_view',
       'abnormality_id', 'abnormality_type', 'mass_shape', 'mass_margins',
       'assessment', 'pathology', 'subtlety', 'image_file_path',
       'cropped_image_file_path', 'ROI_mask_file_path'],
      dtype='object')
In [52]:
count = 0
for i in range(len(mass_data)):
    img = Image.open(mass_data['image_file_path'].iloc[i])

    # Verificamos el modo de color
    if img.mode != 'L':  # 'L' es para escala de grises
        count += 1

print("Tenemos {} imágenes que no están en escala de grises".format(count))
Tenemos 0 imágenes que no están en escala de grises

Redimensionamiento¶

https://www.datacamp.com/es/blog/yolo-object-detection-explained Cambiamos el tamaño de las imagenes a 640X640

In [53]:
import shutil

# Redimensionamos el tamaño de las imagenes del conjunto de entrenamiento
img_height = 640
img_width = 640

shutil.rmtree('Resized_mass_data', ignore_errors=True)
os.makedirs('Resized_mass_data') 
resize_mass_data = []
for i, path in enumerate(mass_data['image_file_path']):
    # Cargamos la imagen en escala de grises
    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)

    resized_img = cv2.resize(img,(img_height,img_width))
    # Guardamos las imagenes redimensionadas
    output_path = f'Resized_mass_data/resized_{i}.jpg'
    cv2.imwrite(output_path, resized_img)
    resize_mass_data.append(output_path)

# Guardamos en el dataset para tener el etiquetado
mass_data['resize_path'] = resize_mass_data

Reducción de ruido¶

Para este apartado he consultado: https://docs.opencv.org/4.x/d5/d69/tutorial_py_non_local_means.html https://docs.opencv.org/4.x/d1/d79/group__photo__denoise.html#ga4c6b0031f56ea3f98f768881279ffe93

In [54]:
# Lo aplicamos a todo el conjunto de entrenamiento 
psnr_values = []
# Guardamos las imagenes filtradas en una nueva ruta
mass_red_ruido = []
shutil.rmtree('Red_Ruido_mass', ignore_errors=True)
os.makedirs('Red_Ruido_mass') 
for i, path in enumerate(mass_data['resize_path']):
    # Cargamos la imagen en escala de grises
    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)

    # Aplicamos reducción de ruido
    img_filtered = cv2.fastNlMeansDenoising(img,3,3,7,21) # Dejamos los valores recomendados por CV

    # Calcular el PSNR entre la imagen original y la imagen filtrada
    current_psnr = psnr(img, img_filtered)
    # Guardar el PSNR para esta imagen 
    psnr_values.append(current_psnr)

    # Guardamos las imagenes preprocesadas
    output_path = f'Red_Ruido_mass/red_ruido_{i}.jpg'
    cv2.imwrite(output_path, img_filtered)
    mass_red_ruido.append(output_path)

mass_data['red_ruido_path'] = mass_red_ruido  
print("El PSNR medio para el conjunto mass es: {}".format(np.mean(psnr_values)))
El PSNR medio para el conjunto mass es: 44.574180066128434
In [55]:
print('Reducción de ruido:\n')
display_images(mass_data, 'red_ruido_path', 5)
Reducción de ruido:

(640, 640, 3)
(640, 640, 3)
(640, 640, 3)
(640, 640, 3)
(640, 640, 3)

Mejora contraste con CLAHE¶

In [56]:
shutil.rmtree('CLAHE_mass', ignore_errors=True)
os.makedirs('CLAHE_mass') 

# Guardamos las nuevas rutas
clahe_mass = []
# Recorremos todo el conjunto de entrenamiento
for i, path in enumerate(mass_data['red_ruido_path']):
    # Cargamos la imagen en escala de grises
    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)

    # Aplicamos el preprocesamiento con CLAHE
    clahe = cv2.createCLAHE()
    cl1 = clahe.apply(img)

    # Guardamos las imagenes preprocesadas
    output_path = f'CLAHE_mass/clahe_{i}.jpg'
    cv2.imwrite(output_path, cl1)
    clahe_mass.append(output_path)

# Guardamos en el dataset para tener el etiquetado
mass_data['clahe_path'] = clahe_mass
In [57]:
print('Mejora de contraste con CLAHE:\n')
display_images(mass_data, 'clahe_path', 5)
print('Imagen con reducción de ruido:\n')
display_images(mass_data, 'red_ruido_path',5)
Mejora de contraste con CLAHE:

(640, 640, 3)
(640, 640, 3)
(640, 640, 3)
(640, 640, 3)
(640, 640, 3)
Imagen con reducción de ruido:

(640, 640, 3)
(640, 640, 3)
(640, 640, 3)
(640, 640, 3)
(640, 640, 3)

Conjuntos entrenamiento y prueba¶

Vamos a separar el conjunto de datos en un conjunto de entrenamiento y otro de prueba. La distribución quedará como el 80\% para el conjunto de entrenamiento y 20\% para el conjunto test

In [58]:
from sklearn.model_selection import train_test_split

# Hacemos los conjuntos de train y test
X_mass = mass_data.drop(columns=['pathology'])
y_mass = mass_data['pathology']

X_train_mass, X_val_mass, y_train_mass, y_val_mass = train_test_split(
    X_mass, y_mass,
    test_size=0.2,
    random_state=42,
    stratify=y_mass
)

# Nos guardamos los datasets
mass_train = pd.concat([X_train_mass, y_train_mass], axis=1)
mass_val = pd.concat([X_val_mass, y_val_mass], axis=1)
In [ ]:
# Comprobamos que la distribución entre conjunto train y test es correcta
fig, ax = plt.subplots()

fig.suptitle("Distibución clases conjunto mass train vs val")

num_mass_train = mass_train['pathology'].value_counts()
num_mass_val = mass_val['pathology'].value_counts() 

ax.bar(num_mass_train.index,num_mass_train, color = "#027a6d",  label = 'train' )
ax.bar(num_mass_val.index, num_mass_val,  color = "#44c27f", label = 'val')
plt.legend()
plt.show()


print("Distribución del conjunto train mass:")
for clase, num in (100 * mass_train['pathology'].value_counts() / (mass_train['pathology'].value_counts() + mass_val['pathology'].value_counts())).items():
    print(f"{clase}: {num:.2f}%")
print("Distribución del conjunto val mass:")
for clase, num in (100 * mass_val['pathology'].value_counts() / (mass_train['pathology'].value_counts() + mass_val['pathology'].value_counts())).items():
    print(f"{clase}: {num:.2f}%")
Distribución del conjunto train mass:
BENIGN: 79.93%
MALIGNANT: 79.97%
Distribución del conjunto val mass:
BENIGN: 20.07%
MALIGNANT: 20.03%

Detección de anomalías con YOLO¶

Primero organizamos las imagenes en carpetas de entrenamiento y test como espaera YOLO, además de otra carpeta con las etiquetas de las imagenes de entrenamiento y test. https://docs.ultralytics.com/es/datasets/detect/#ultralytics-yolo-format

In [ ]:
# yolo espera que las etiquetas de clase sean numericas
label_map = {'BENIGN': 0, 'MALIGNANT': 1}
mass_data['pathology'] = mass_data['pathology'].map(label_map)
In [ ]:
mass_train['pathology'] = mass_train['pathology'].map(label_map)
mass_val['pathology'] = mass_val['pathology'].map(label_map)
In [ ]:
# Creamos la estructura 
base_dir = 'final_dataset_yolo'

for split in ['train', 'val']:
    os.makedirs(os.path.join(base_dir, 'images', split), exist_ok=True)
    os.makedirs(os.path.join(base_dir, 'labels', split), exist_ok=True)

Se extraen los bounding boxes que necesita YOLO https://stackoverflow.com/questions/73282135/computing-bounding-boxes-from-a-mask-image-tensorflow-or-other

In [ ]:
from tqdm import tqdm
from shutil import copyfile
import pydicom
from skimage.measure import label, regionprops, find_contours

def mask_to_bbox(mask, min_area=50):
    bboxes = []
    
    # Asegurar que la máscara es binaria
    _, mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)

    # Etiquetar regiones conectadas
    lbl = label(mask)
    props = regionprops(lbl)

    for prop in props:
        if prop.area < min_area:
            continue

        x1 = prop.bbox[1]
        y1 = prop.bbox[0]
        x2 = prop.bbox[3]
        y2 = prop.bbox[2]

        bboxes.append([x1, y1, x2, y2])

    return bboxes


def create_yolo_labels(bboxes, img_w, img_h, label_path, class_id):
    with open(label_path, 'w') as f: 
        for bbox in bboxes: 
            x_min, y_min, x_max, y_max = bbox  
            x_center = ((x_min + x_max) / 2) / img_w  
            y_center = ((y_min + y_max) / 2) / img_h  
            width = (x_max - x_min) / img_w  
            height = (y_max - y_min) / img_h  
            f.write(f"{class_id} {x_center} {y_center} {width} {height}\n")
# Se crea el .txt para YOLO de todas las imagenes
def process_split(df, split, base_dir):

    for idx, row in tqdm(df.iterrows(), total=len(df), desc=f"Processing {split}"):
        # Conseguimos la ruta de las imagenes y sus mascaras
        mask_path = row['ROI_mask_file_path']
        img_path = row['clahe_path']
        class_id = row['pathology']


        img_dest = os.path.join(base_dir, 'images', split, f"{idx}.jpg")
        os.makedirs(os.path.dirname(img_dest), exist_ok=True)
        copyfile(img_path, img_dest)


        img = Image.open(img_path)
        img_w, img_h = img.size

        # Leer máscara y obtener bboxes
        mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
        mask = cv2.resize(mask, (img_w, img_h), interpolation=cv2.INTER_NEAREST)
        bboxes = mask_to_bbox(mask)

        # Crear .txt con anotaciones YOLO
        label_dest = os.path.join(base_dir, 'labels', split, f"{idx}.txt")
        os.makedirs(os.path.dirname(label_dest), exist_ok=True)
        create_yolo_labels(bboxes, img_w, img_h, label_dest, class_id)
In [ ]:
process_split(mass_train, 'train', base_dir)
Processing train: 100%|██████████| 1356/1356 [00:55<00:00, 24.64it/s]
In [ ]:
process_split(mass_val, 'val', base_dir)
Processing val: 100%|██████████| 340/340 [00:13<00:00, 24.68it/s]
In [ ]:
# Creamos el archivo yaml

content = f"""train: {os.path.abspath(base_dir)}/images/train
val: {os.path.abspath(base_dir)}/images/val
nc: 2
names: ['BENIGN', 'MALIGNANT']
"""
with open(os.path.join(base_dir, 'data.yaml'), 'w') as f:
    f.write(content)
In [ ]:
image_path = '/home/mperpinav/final_dataset_yolo/images/val/300.jpg'
label_path = '/home/mperpinav/final_dataset_yolo/labels/val/300.txt'
import cv2
import os
import matplotlib.pyplot as plt

def draw_yolo_boxes(image_path, label_path):
    img = cv2.imread(image_path)
    if img is None:
        raise ValueError(f"Imagen no encontrada o no compatible: {image_path}")
    
    h, w = img.shape[:2]
    print(f"Imagen cargada: {image_path} - tamaño: {w}x{h}")

    if not os.path.exists(label_path):
        print("No existe archivo de labels:", label_path)
        return img

    with open(label_path, 'r') as f:
        for line in f:
            parts = line.strip().split()
            if len(parts) != 5:
                print("Línea inválida:", line)
                continue

            class_id, x_center, y_center, box_w, box_h = map(float, parts)

            # Convertir a coordenadas de píxeles
            x_center *= w
            y_center *= h
            box_w *= w
            box_h *= h

            x1 = int(x_center - box_w / 2)
            y1 = int(y_center - box_h / 2)
            x2 = int(x_center + box_w / 2)
            y2 = int(y_center + box_h / 2)

            print(f"Dibujando caja clase {int(class_id)}: ({x1}, {y1}) - ({x2}, {y2})")
            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)

    return img

def show_image(img):
    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    plt.figure(figsize=(10, 10))
    plt.imshow(img_rgb)
    plt.axis('off')
    plt.show()

img_with_boxes = draw_yolo_boxes(image_path, label_path)

show_image(img_with_boxes)
Imagen cargada: /home/mperpinav/final_dataset_yolo/images/val/300.jpg - tamaño: 640x640
Dibujando caja clase 0: (411, 339) - (461, 365)

YOLO v8¶

In [ ]:
print(base_dir)
final_dataset_yolo
In [ ]:
# Entrenamiento modelo mass YOLO v8


model = YOLO('yolov8n.pt')
data_mass_path = os.path.join(base_dir, 'data.yaml')
model.train(data=data_mass_path, epochs=100, patience = 10)
New https://pypi.org/project/ultralytics/8.3.125 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=final_dataset_yolo/data.yaml, epochs=100, time=None, patience=10, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train40, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train40
Overriding model.yaml nc=80 with nc=2

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    751702  ultralytics.nn.modules.head.Detect           [2, [64, 128, 256]]           
Model summary: 129 layers, 3,011,238 parameters, 3,011,222 gradients, 8.2 GFLOPs

Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 951.3±332.0 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:01<00:00, 663.22it/s]
train: New cache created: /home/mperpinav/final_dataset_yolo/labels/train.cache

val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 579.8±427.2 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<00:00, 687.47it/s]
val: New cache created: /home/mperpinav/final_dataset_yolo/labels/val.cache
Plotting labels to runs/detect/train40/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/train40
Starting training for 100 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      1/100      2.19G      2.348      4.978      1.714          6        640: 100%|██████████| 71/71 [00:16<00:00,  4.35it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.50it/s]
                   all        324        324    0.00115      0.346     0.0322     0.0119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      2/100      2.47G      2.183       3.88      1.626          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.92it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.85it/s]
                   all        324        324       0.15      0.118     0.0619      0.023

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      3/100      2.49G      2.203      3.419      1.614          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.84it/s]
                   all        324        324     0.0884      0.141     0.0609     0.0221

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      4/100       2.5G      2.123      3.086       1.59          8        640: 100%|██████████| 71/71 [00:14<00:00,  5.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.88it/s]
                   all        324        324       0.19      0.204      0.106     0.0389

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      5/100      2.51G      2.182      2.999      1.599          7        640: 100%|██████████| 71/71 [00:14<00:00,  5.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.73it/s]
                   all        324        324      0.247      0.171      0.141     0.0578

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      6/100      2.54G      2.108       2.81      1.598          5        640: 100%|██████████| 71/71 [00:14<00:00,  5.05it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.00it/s]
                   all        324        324      0.201      0.372      0.175     0.0658

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      7/100      2.55G       2.14      2.703      1.607          3        640: 100%|██████████| 71/71 [00:14<00:00,  5.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.13it/s]
                   all        324        324      0.311       0.27      0.191     0.0734

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      8/100      2.57G      2.074      2.672      1.616          6        640: 100%|██████████| 71/71 [00:14<00:00,  5.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.03it/s]
                   all        324        324      0.277      0.251       0.19     0.0739

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      9/100      2.58G      2.045      2.624      1.536          7        640: 100%|██████████| 71/71 [00:14<00:00,  5.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.02it/s]
                   all        324        324      0.289      0.311      0.204     0.0779

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     10/100       2.6G      2.091      2.653      1.584          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]
                   all        324        324      0.293      0.344      0.236     0.0989

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     11/100      2.62G      2.047      2.537      1.546          3        640: 100%|██████████| 71/71 [00:14<00:00,  5.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]
                   all        324        324      0.284      0.333      0.198     0.0776

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     12/100      2.64G      2.026      2.566      1.532          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.95it/s]
                   all        324        324      0.344      0.346      0.248      0.102

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     13/100      2.65G       2.06      2.593      1.573          4        640: 100%|██████████| 71/71 [00:14<00:00,  5.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.95it/s]
                   all        324        324      0.276      0.365      0.221     0.0821

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     14/100      2.67G       2.06      2.518      1.582          5        640: 100%|██████████| 71/71 [00:14<00:00,  5.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.02it/s]
                   all        324        324      0.333      0.326      0.232     0.0872

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     15/100      2.69G      2.029      2.495       1.56          4        640: 100%|██████████| 71/71 [00:14<00:00,  5.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324       0.31      0.352      0.235     0.0941

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     16/100      2.71G      1.996      2.415      1.534          5        640: 100%|██████████| 71/71 [00:14<00:00,  5.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.93it/s]
                   all        324        324      0.375      0.344      0.279      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     17/100      2.71G      2.023      2.447       1.54          6        640: 100%|██████████| 71/71 [00:14<00:00,  5.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.01it/s]
                   all        324        324      0.315       0.33      0.221     0.0895

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     18/100      2.74G      2.003      2.454      1.567          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]
                   all        324        324      0.351      0.377      0.282      0.115

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     19/100      2.76G      1.971      2.409       1.53          8        640: 100%|██████████| 71/71 [00:14<00:00,  5.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.11it/s]
                   all        324        324      0.312      0.343      0.247     0.0971

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     20/100      2.77G      1.986       2.39      1.524          4        640: 100%|██████████| 71/71 [00:14<00:00,  5.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.97it/s]
                   all        324        324      0.272      0.351      0.242     0.0931

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     21/100      2.78G      2.017       2.44      1.586          5        640: 100%|██████████| 71/71 [00:14<00:00,  5.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.06it/s]
                   all        324        324      0.346      0.381      0.268       0.11

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     22/100      2.81G      1.961      2.281      1.523          4        640: 100%|██████████| 71/71 [00:14<00:00,  5.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.94it/s]
                   all        324        324      0.332      0.394      0.261     0.0998

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     23/100      2.82G      1.974      2.384      1.543          5        640: 100%|██████████| 71/71 [00:14<00:00,  5.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.98it/s]
                   all        324        324      0.353      0.354      0.283      0.105

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     24/100      2.84G      1.956      2.424      1.573          5        640: 100%|██████████| 71/71 [00:14<00:00,  5.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.10it/s]
                   all        324        324      0.372      0.386      0.279      0.116

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     25/100      2.85G      1.919      2.333       1.51          9        640: 100%|██████████| 71/71 [00:14<00:00,  5.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.371      0.361      0.293      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     26/100      2.88G      1.895      2.275      1.486         10        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.88it/s]
                   all        324        324      0.371      0.359      0.302      0.121

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     27/100      2.89G      1.924       2.32      1.508          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]
                   all        324        324      0.379      0.426      0.303       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     28/100      2.91G       1.93      2.288      1.525          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.04it/s]
                   all        324        324      0.398      0.403      0.328      0.129

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     29/100      2.92G      1.907      2.303      1.508          9        640: 100%|██████████| 71/71 [00:14<00:00,  5.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.06it/s]
                   all        324        324      0.374      0.382      0.301       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     30/100      2.95G      1.921      2.298      1.488         10        640: 100%|██████████| 71/71 [00:14<00:00,  5.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.05it/s]
                   all        324        324      0.403      0.412      0.303      0.117

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     31/100      2.96G      1.913      2.204      1.511          7        640: 100%|██████████| 71/71 [00:14<00:00,  5.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324      0.369      0.394      0.298      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     32/100      2.98G      1.893      2.241       1.47          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]
                   all        324        324      0.331      0.386      0.272      0.111

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     33/100      2.99G      1.892      2.138      1.487          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.95it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.05it/s]
                   all        324        324      0.387      0.359      0.317      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     34/100      3.01G        1.9      2.248       1.49          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.93it/s]
                   all        324        324      0.401      0.387      0.315      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     35/100      3.03G      1.892      2.149      1.505          4        640: 100%|██████████| 71/71 [00:14<00:00,  5.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.00it/s]
                   all        324        324       0.36      0.345      0.296      0.116

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     36/100      3.05G      1.901      2.149      1.494          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.94it/s]
                   all        324        324      0.359      0.354      0.285       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     37/100      3.05G      1.848       2.18      1.464          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.95it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.01it/s]
                   all        324        324      0.395      0.345      0.299      0.123

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     38/100      3.08G      1.925      2.189      1.515          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.79it/s]
                   all        324        324      0.434      0.374      0.309      0.123
EarlyStopping: Training stopped early as no improvement observed in last 10 epochs. Best results observed at epoch 28, best model saved as best.pt.
To update EarlyStopping(patience=10) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.

38 epochs completed in 0.185 hours.
Optimizer stripped from runs/detect/train40/weights/last.pt, 6.2MB
Optimizer stripped from runs/detect/train40/weights/best.pt, 6.2MB

Validating runs/detect/train40/weights/best.pt...
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
Model summary (fused): 72 layers, 3,006,038 parameters, 0 gradients, 8.1 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.31it/s]
                   all        324        324      0.402      0.401      0.328      0.129
                BENIGN        174        174      0.416      0.336      0.319       0.13
             MALIGNANT        150        150      0.387      0.467      0.337      0.128
Speed: 0.2ms preprocess, 2.6ms inference, 0.0ms loss, 2.0ms postprocess per image
Results saved to runs/detect/train40
Out[ ]:
ultralytics.utils.metrics.DetMetrics object with attributes:

ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7fc6ca910d30>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,
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           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.93678,     0.93678,     0.91379, ...,           0,           0,           0],
       [    0.90667,     0.90667,        0.88, ...,           0,           0,           0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.1490300014987881)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([    0.13011,     0.12827])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.4015510104609523), 'metrics/recall(B)': np.float64(0.40146752355804083), 'metrics/mAP50(B)': np.float64(0.3275996103629044), 'metrics/mAP50-95(B)': np.float64(0.1291889338472196), 'fitness': np.float64(0.1490300014987881)}
save_dir: PosixPath('runs/detect/train40')
speed: {'preprocess': 0.17833083086175683, 'inference': 2.645663356546451, 'loss': 0.006058980584328557, 'postprocess': 1.962173750256131}
task: 'detect'
In [339]:
# Entrenamiento YOLOv8
!ls runs/detect/train40
/etc/bash.bashrc: line 5: export: `:/home/mperpinav/.local/lib/python3.10/site-packages/cv2/../../lib64:/usr/local/cuda-12.2/lib64': not a valid identifier
args.yaml			 PR_curve.png	   val_batch0_labels.jpg
confusion_matrix_normalized.png  R_curve.png	   val_batch0_pred.jpg
confusion_matrix.png		 results.csv	   val_batch1_labels.jpg
F1_curve.png			 results.png	   val_batch1_pred.jpg
labels_correlogram.jpg		 train_batch0.jpg  val_batch2_labels.jpg
labels.jpg			 train_batch1.jpg  val_batch2_pred.jpg
P_curve.png			 train_batch2.jpg  weights

YOLOv11¶

In [336]:
# Entrenamiento modelo mass YOLO v11

model_11 = YOLO("yolo11n.pt")
data_mass_path = os.path.join(base_dir, 'data.yaml')
model_11.train(data=data_mass_path, epochs=100, patience = 10)
New https://pypi.org/project/ultralytics/8.3.125 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
engine/trainer: task=detect, mode=train, model=yolo11n.pt, data=final_dataset_yolo/data.yaml, epochs=100, time=None, patience=10, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train41, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train41
Overriding model.yaml nc=80 with nc=2

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      
  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     
  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           
 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          
 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           
 23        [16, 19, 22]  1    431062  ultralytics.nn.modules.head.Detect           [2, [64, 128, 256]]           
YOLO11n summary: 181 layers, 2,590,230 parameters, 2,590,214 gradients, 6.4 GFLOPs

Transferred 448/499 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 617.5±215.7 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 407.5±257.2 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train41/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/train41
Starting training for 100 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      1/100       2.8G      2.357       5.12      1.706          6        640: 100%|██████████| 71/71 [00:22<00:00,  3.14it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.35it/s]
                   all        324        324      0.486     0.0157     0.0921     0.0268

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      2/100      3.04G      2.201      3.997      1.608          4        640: 100%|██████████| 71/71 [00:18<00:00,  3.91it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.37it/s]
                   all        324        324      0.117      0.197     0.0686     0.0216

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      3/100      3.04G      2.211      3.502       1.61          8        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.71it/s]
                   all        324        324      0.165      0.247      0.114     0.0385

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      4/100      3.04G      2.167      3.144      1.613          8        640: 100%|██████████| 71/71 [00:17<00:00,  4.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.68it/s]
                   all        324        324      0.187     0.0954      0.112     0.0446

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      5/100      3.04G       2.19      2.974      1.645          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.67it/s]
                   all        324        324      0.217      0.287      0.155     0.0557

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      6/100      3.04G      2.108      2.802      1.622          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.69it/s]
                   all        324        324      0.205        0.3      0.134      0.053

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      7/100      3.04G      2.125      2.757      1.626          3        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.79it/s]
                   all        324        324      0.201      0.252      0.133     0.0448

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      8/100      3.04G      2.101        2.7      1.644          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324      0.299      0.269      0.201      0.078

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      9/100      3.04G      2.038      2.637      1.559          7        640: 100%|██████████| 71/71 [00:19<00:00,  3.57it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324      0.303      0.348      0.213     0.0785

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     10/100      3.04G        2.1      2.671      1.605          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.44it/s]
                   all        324        324      0.295      0.265      0.181     0.0757

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     11/100      3.04G      2.089      2.565      1.603          3        640: 100%|██████████| 71/71 [00:17<00:00,  4.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.80it/s]
                   all        324        324      0.239      0.257      0.156     0.0624

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     12/100      3.04G      2.048      2.595       1.57          6        640: 100%|██████████| 71/71 [00:18<00:00,  3.94it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.65it/s]
                   all        324        324      0.348       0.27      0.225     0.0894

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     13/100      3.04G      2.063      2.598      1.571          4        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.63it/s]
                   all        324        324      0.329      0.357      0.268       0.11

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     14/100      3.04G      2.067      2.512      1.585          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.77it/s]
                   all        324        324      0.296      0.333      0.221     0.0884

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     15/100      3.04G      2.054       2.51      1.588          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.75it/s]
                   all        324        324      0.317      0.302      0.243     0.0871

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     16/100      3.04G      2.013      2.431      1.573          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324      0.326      0.351      0.258      0.105

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     17/100      3.04G      2.041      2.441      1.561          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.79it/s]
                   all        324        324      0.259      0.284      0.171     0.0667

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     18/100      3.04G       1.98      2.437      1.566          9        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324      0.389      0.356      0.289      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     19/100      3.04G       1.98      2.425      1.547          8        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.51it/s]
                   all        324        324      0.347      0.329      0.247      0.103

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     20/100      3.04G      1.989       2.41      1.534          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.81it/s]
                   all        324        324      0.348      0.319      0.234     0.0926

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     21/100      3.04G      2.007      2.423      1.584          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324      0.385      0.316      0.242      0.101

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     22/100      3.04G      1.976      2.304      1.524          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.87it/s]
                   all        324        324      0.386      0.352      0.286      0.113

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     23/100      3.04G      1.998      2.394      1.556          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.77it/s]
                   all        324        324      0.345      0.338      0.283      0.112

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     24/100      3.04G      1.996      2.447      1.608          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.97it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.57it/s]
                   all        324        324       0.34      0.351      0.264      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     25/100      3.04G      1.951      2.336      1.528          9        640: 100%|██████████| 71/71 [00:17<00:00,  3.95it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324      0.322      0.347      0.252     0.0989

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     26/100      3.04G      1.922      2.303      1.509         10        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.77it/s]
                   all        324        324      0.338      0.339      0.281      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     27/100      3.04G      1.958      2.361      1.529          8        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.69it/s]
                   all        324        324      0.456      0.355      0.294      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     28/100      3.04G      1.954      2.337      1.539          3        640: 100%|██████████| 71/71 [00:17<00:00,  3.97it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.85it/s]
                   all        324        324      0.367      0.284      0.266      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     29/100      3.04G      1.925      2.329      1.528          9        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.77it/s]
                   all        324        324      0.336      0.342      0.246        0.1

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     30/100      3.04G       1.93      2.341      1.509         10        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.81it/s]
                   all        324        324      0.366      0.399      0.314      0.119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     31/100      3.04G      1.917       2.28       1.51          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.49it/s]
                   all        324        324      0.409      0.402      0.304      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     32/100      3.04G      1.932       2.26      1.484          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324      0.306      0.375      0.302      0.121

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     33/100      3.04G      1.921      2.196      1.503          3        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324       0.39      0.358      0.309      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     34/100      3.04G      1.923      2.295      1.493          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.63it/s]
                   all        324        324      0.374      0.348      0.259      0.106

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     35/100      3.04G      1.892      2.173      1.501          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.63it/s]
                   all        324        324      0.388      0.361      0.313      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     36/100      3.04G      1.932      2.206      1.505          8        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324       0.39      0.319      0.283      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     37/100      3.04G      1.871      2.222      1.464          8        640: 100%|██████████| 71/71 [00:17<00:00,  3.96it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.72it/s]
                   all        324        324      0.424       0.38      0.324      0.129

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     38/100      3.04G      1.935      2.248      1.516          7        640: 100%|██████████| 71/71 [00:20<00:00,  3.40it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.74it/s]
                   all        324        324        0.4      0.396      0.326      0.134

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     39/100      3.04G      1.871      2.192      1.464          7        640: 100%|██████████| 71/71 [00:18<00:00,  3.81it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.52it/s]
                   all        324        324      0.278      0.323      0.233     0.0973

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     40/100      3.04G      1.925      2.223        1.5          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.67it/s]
                   all        324        324      0.417      0.387      0.305      0.121

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     41/100      3.04G      1.888      2.135      1.502          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.96it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324      0.367      0.363      0.299      0.119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     42/100      3.04G      1.926       2.17      1.529          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.51it/s]
                   all        324        324      0.375      0.347      0.269      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     43/100      3.04G      1.863      2.193      1.465          9        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.72it/s]
                   all        324        324      0.333       0.42      0.279       0.11

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     44/100      3.04G      1.876      2.143      1.469          7        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.80it/s]
                   all        324        324      0.388      0.391      0.309      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     45/100      3.04G      1.878      2.113      1.485          3        640: 100%|██████████| 71/71 [00:17<00:00,  3.95it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.77it/s]
                   all        324        324      0.401      0.384      0.346      0.145

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     46/100      3.04G      1.834      2.091      1.472          4        640: 100%|██████████| 71/71 [00:17<00:00,  3.97it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.85it/s]
                   all        324        324       0.42      0.315      0.301      0.129

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     47/100      3.04G      1.853      2.056      1.467          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.81it/s]
                   all        324        324      0.369      0.347      0.305      0.123

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     48/100      3.04G      1.864      2.051      1.491          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324      0.408      0.378      0.315      0.127

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     49/100      3.04G      1.803      2.103      1.445          3        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.64it/s]
                   all        324        324      0.421      0.383      0.322      0.137

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     50/100      3.04G      1.858      2.072      1.484          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324       0.41      0.395      0.308      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     51/100      3.04G      1.814      2.063      1.441          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324      0.402      0.368      0.311      0.125

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     52/100      3.04G       1.82      2.018      1.432          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.81it/s]
                   all        324        324       0.38      0.397      0.311      0.125

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     53/100      3.04G      1.826      2.007      1.456          4        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324      0.421      0.394      0.319      0.132

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     54/100      3.04G      1.789      2.014       1.44          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.80it/s]
                   all        324        324      0.367      0.388      0.296      0.123

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     55/100      3.04G       1.78      1.976      1.418          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324      0.348      0.404      0.315      0.129
EarlyStopping: Training stopped early as no improvement observed in last 10 epochs. Best results observed at epoch 45, best model saved as best.pt.
To update EarlyStopping(patience=10) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.

55 epochs completed in 0.330 hours.
Optimizer stripped from runs/detect/train41/weights/last.pt, 5.5MB
Optimizer stripped from runs/detect/train41/weights/best.pt, 5.5MB

Validating runs/detect/train41/weights/best.pt...
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
YOLO11n summary (fused): 100 layers, 2,582,542 parameters, 0 gradients, 6.3 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  1.90it/s]
                   all        324        324      0.402      0.384      0.345      0.144
                BENIGN        174        174      0.428      0.328       0.34      0.145
             MALIGNANT        150        150      0.377       0.44      0.351      0.144
Speed: 0.2ms preprocess, 3.2ms inference, 0.0ms loss, 3.1ms postprocess per image
Results saved to runs/detect/train41
Out[336]:
ultralytics.utils.metrics.DetMetrics object with attributes:

ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7fc6cbaf2680>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,
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           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,
           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,
           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,
           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,
           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,
           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,
           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,
           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,
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           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,
           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,
            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,
           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,
           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,
           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,
            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,
           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,
           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,
           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,
            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,
           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,
           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,
           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,
           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,
           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,
           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,
           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,
           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,
           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,
           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,
           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,
           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[     0.9023,      0.9023,     0.88506, ...,           0,           0,           0],
       [        0.9,         0.9,     0.87333, ...,           0,           0,           0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.1642880041285661)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([    0.14487,     0.14351])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.40246863379133063), 'metrics/recall(B)': np.float64(0.3837931034482759), 'metrics/mAP50(B)': np.float64(0.34517281147325807), 'metrics/mAP50-95(B)': np.float64(0.1441896922013781), 'fitness': np.float64(0.1642880041285661)}
save_dir: PosixPath('runs/detect/train41')
speed: {'preprocess': 0.20064869723110287, 'inference': 3.2458971082060426, 'loss': 0.002713595160547598, 'postprocess': 3.0774328610457387}
task: 'detect'
In [ ]:
# Entrenamiento YOLOv11
!ls runs/detect/train41
/etc/bash.bashrc: line 5: export: `:/home/mperpinav/.local/lib/python3.10/site-packages/cv2/../../lib64:/usr/local/cuda-12.2/lib64': not a valid identifier
args.yaml			 PR_curve.png	   val_batch0_labels.jpg
confusion_matrix_normalized.png  R_curve.png	   val_batch0_pred.jpg
confusion_matrix.png		 results.csv	   val_batch1_labels.jpg
F1_curve.png			 results.png	   val_batch1_pred.jpg
labels_correlogram.jpg		 train_batch0.jpg  val_batch2_labels.jpg
labels.jpg			 train_batch1.jpg  val_batch2_pred.jpg
P_curve.png			 train_batch2.jpg  weights

YOLO v5¶

https://docs.ultralytics.com/es/yolov5/tutorials/train_custom_data/#local-logging

In [55]:
# Entrenamiento modelo mass YOLO v5

model_55 = YOLO("yolov5n.pt")
data_mass_path = os.path.join('final_dataset_yolo', 'data.yaml')
model_55.train(data=data_mass_path, epochs=100, patience = 10)
PRO TIP 💡 Replace 'model=yolov5n.pt' with new 'model=yolov5nu.pt'.
YOLOv5 'u' models are trained with https://github.com/ultralytics/ultralytics and feature improved performance vs standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.

Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5nu.pt to 'yolov5nu.pt'...
100%|██████████| 5.31M/5.31M [00:02<00:00, 2.07MB/s]
New https://pypi.org/project/ultralytics/8.3.126 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
engine/trainer: task=detect, mode=train, model=yolov5n.pt, data=final_dataset_yolo/data.yaml, epochs=100, time=None, patience=10, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train43, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train43
Overriding model.yaml nc=80 with nc=2

                   from  n    params  module                                       arguments                     
  0                  -1  1      1760  ultralytics.nn.modules.conv.Conv             [3, 16, 6, 2, 2]              
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      4800  ultralytics.nn.modules.block.C3              [32, 32, 1]                   
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     29184  ultralytics.nn.modules.block.C3              [64, 64, 2]                   
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  3    156928  ultralytics.nn.modules.block.C3              [128, 128, 3]                 
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    296448  ultralytics.nn.modules.block.C3              [256, 256, 1]                 
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1     33024  ultralytics.nn.modules.conv.Conv             [256, 128, 1, 1]              
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  1     90880  ultralytics.nn.modules.block.C3              [256, 128, 1, False]          
 14                  -1  1      8320  ultralytics.nn.modules.conv.Conv             [128, 64, 1, 1]               
 15                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 16             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 17                  -1  1     22912  ultralytics.nn.modules.block.C3              [128, 64, 1, False]           
 18                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 19            [-1, 14]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 20                  -1  1     74496  ultralytics.nn.modules.block.C3              [128, 128, 1, False]          
 21                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 22            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 23                  -1  1    296448  ultralytics.nn.modules.block.C3              [256, 256, 1, False]          
 24        [17, 20, 23]  1    751702  ultralytics.nn.modules.head.Detect           [2, [64, 128, 256]]           
YOLOv5n summary: 153 layers, 2,508,854 parameters, 2,508,838 gradients, 7.2 GFLOPs

Transferred 391/427 items from pretrained weights
Freezing layer 'model.24.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 1028.2±295.5 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 490.8±390.7 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train43/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 69 weight(decay=0.0), 76 weight(decay=0.0005), 75 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/train43
Starting training for 100 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      1/100      2.17G      2.345      5.011      1.756          6        640: 100%|██████████| 71/71 [00:19<00:00,  3.71it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.54it/s]
                   all        324        324    0.00167      0.499     0.0577     0.0169

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      2/100      2.44G      2.173      3.801      1.582          4        640: 100%|██████████| 71/71 [00:15<00:00,  4.65it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.07it/s]
                   all        324        324      0.233      0.167      0.103      0.036

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      3/100      2.45G      2.219       3.33      1.636          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.97it/s]
                   all        324        324      0.222      0.324      0.152     0.0582

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      4/100      2.46G      2.199      3.084      1.638          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.198      0.242      0.145     0.0539

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      5/100      2.47G      2.168      2.955      1.584          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.164      0.169     0.0897     0.0333

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      6/100      2.49G      2.147      2.815      1.639          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.11it/s]
                   all        324        324      0.252      0.307      0.171     0.0659

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      7/100      2.49G      2.152      2.769      1.629          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.07it/s]
                   all        324        324      0.282       0.29      0.172     0.0648

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      8/100      2.49G      2.081      2.729      1.611          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.10it/s]
                   all        324        324      0.272      0.231      0.174     0.0716

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      9/100      2.49G      2.045      2.654      1.553          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.313      0.294      0.208      0.077

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     10/100      2.49G      2.087      2.655      1.579          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.81it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.00it/s]
                   all        324        324      0.307      0.336      0.221     0.0887

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     11/100      2.49G      2.065      2.552      1.603          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.00it/s]
                   all        324        324      0.269      0.357      0.216     0.0834

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     12/100      2.51G       2.04      2.576       1.56          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.89it/s]
                   all        324        324       0.33      0.333      0.224     0.0878

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     13/100      2.51G      2.048      2.617       1.57          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.04it/s]
                   all        324        324      0.316      0.313      0.226     0.0933

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     14/100      2.54G      2.052      2.574      1.584          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.93it/s]
                   all        324        324      0.336      0.338      0.233     0.0899

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     15/100      2.55G      2.067      2.516      1.581          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.90it/s]
                   all        324        324      0.311      0.292       0.22     0.0816

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     16/100      2.56G      2.024      2.475      1.546          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.04it/s]
                   all        324        324      0.346      0.313      0.253      0.102

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     17/100      2.56G      2.051      2.507      1.566          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.01it/s]
                   all        324        324      0.325      0.264      0.213     0.0876

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     18/100      2.56G      2.006      2.505      1.574          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.17it/s]
                   all        324        324      0.344      0.354      0.252     0.0977

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     19/100      2.56G      1.966      2.433      1.545          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.86it/s]
                   all        324        324      0.329       0.33      0.232     0.0898

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     20/100      2.56G          2      2.401      1.525          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.96it/s]
                   all        324        324      0.391      0.339      0.273      0.108

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     21/100      2.56G      1.997      2.489      1.549          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.06it/s]
                   all        324        324      0.355      0.354      0.266      0.108

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     22/100      2.56G      1.999      2.352      1.545          4        640: 100%|██████████| 71/71 [00:16<00:00,  4.31it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.62it/s]
                   all        324        324      0.379      0.369      0.284      0.106

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     23/100      2.56G      1.972      2.397      1.541          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.81it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.66it/s]
                   all        324        324      0.337      0.345      0.266      0.105

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     24/100      2.56G      1.972      2.422      1.567          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.11it/s]
                   all        324        324      0.349      0.376      0.267      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     25/100      2.56G      1.949      2.376      1.529          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324      0.335       0.37      0.282      0.114

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     26/100      2.56G      1.916      2.349      1.478         10        640: 100%|██████████| 71/71 [00:15<00:00,  4.58it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.92it/s]
                   all        324        324      0.391      0.235       0.23     0.0953

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     27/100      2.56G       1.96      2.342      1.522          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.79it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.90it/s]
                   all        324        324      0.345      0.372      0.283      0.108

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     28/100      2.56G      1.939      2.355      1.512          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.06it/s]
                   all        324        324      0.422       0.26      0.225     0.0798

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     29/100      2.56G      1.947      2.381      1.545          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.11it/s]
                   all        324        324      0.306      0.346      0.254      0.104

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     30/100      2.56G      1.928       2.34      1.497         10        640: 100%|██████████| 71/71 [00:15<00:00,  4.70it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.356      0.451      0.291      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     31/100      2.56G      1.921      2.243      1.513          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.02it/s]
                   all        324        324      0.373      0.378      0.302      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     32/100      2.56G      1.922      2.264      1.469          5        640: 100%|██████████| 71/71 [00:15<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.11it/s]
                   all        324        324      0.373      0.387      0.286      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     33/100      2.56G      1.907      2.212      1.497          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324      0.404      0.331        0.3      0.123

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     34/100      2.56G      1.906      2.299      1.477          6        640: 100%|██████████| 71/71 [00:15<00:00,  4.72it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324      0.374      0.363       0.28      0.113

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     35/100      2.56G       1.92      2.176      1.517          4        640: 100%|██████████| 71/71 [00:16<00:00,  4.35it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.52it/s]
                   all        324        324      0.325      0.352      0.236     0.0952

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     36/100      2.56G      1.931      2.207      1.523          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.02it/s]
                   all        324        324      0.379      0.368      0.276      0.113

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     37/100      2.56G      1.898      2.218      1.478          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.81it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.305      0.416      0.266      0.111

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     38/100      2.56G       1.94      2.288      1.509          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324      0.406      0.389      0.294      0.114

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     39/100      2.56G      1.865      2.213      1.472          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.341      0.313      0.247     0.0997

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     40/100      2.56G      1.928      2.225        1.5          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.07it/s]
                   all        324        324      0.448      0.384      0.306      0.127

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     41/100      2.56G      1.871      2.133       1.47          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.13it/s]
                   all        324        324      0.335      0.364      0.273      0.115

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     42/100      2.56G      1.925      2.152      1.523          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.96it/s]
                   all        324        324      0.384      0.357      0.297      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     43/100      2.56G      1.849      2.163      1.443          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.11it/s]
                   all        324        324      0.364      0.378      0.309      0.125

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     44/100      2.56G      1.861      2.168      1.456          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.75it/s]
                   all        324        324      0.406      0.401      0.304      0.119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     45/100      2.56G      1.857      2.118       1.48          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.75it/s]
                   all        324        324      0.353      0.372      0.279      0.107

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     46/100      2.56G       1.82      2.114      1.456          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.10it/s]
                   all        324        324      0.395      0.365      0.304      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     47/100      2.56G      1.832      2.031       1.45          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.71it/s]
                   all        324        324       0.35      0.413      0.278      0.115

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     48/100      2.56G      1.831      2.017      1.473          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324      0.363      0.433      0.308      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     49/100      2.56G      1.807      2.055      1.434          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324      0.407      0.361      0.284      0.119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     50/100      2.56G      1.828      2.052      1.461          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.477      0.388      0.353      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     51/100      2.56G       1.81      2.024      1.432          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324       0.35      0.369        0.3       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     52/100      2.56G       1.82      2.021       1.42          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324      0.441      0.362      0.282      0.114

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     53/100      2.56G      1.798       2.01      1.438          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.403      0.422      0.322      0.127

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     54/100      2.56G        1.8      2.003      1.431          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.90it/s]
                   all        324        324      0.425      0.349      0.317      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     55/100      2.56G      1.761      1.918      1.397          6        640: 100%|██████████| 71/71 [00:15<00:00,  4.64it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.92it/s]
                   all        324        324       0.41      0.381      0.309      0.121

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     56/100      2.56G      1.751      1.954      1.412          7        640: 100%|██████████| 71/71 [00:15<00:00,  4.72it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.87it/s]
                   all        324        324      0.448      0.343      0.319      0.132

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     57/100      2.56G      1.811      1.971       1.43          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.17it/s]
                   all        324        324      0.364      0.398      0.322      0.134

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     58/100      2.56G      1.772      1.914      1.419          6        640: 100%|██████████| 71/71 [00:16<00:00,  4.26it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.376      0.354      0.294       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     59/100      2.56G      1.749      1.949       1.43          4        640: 100%|██████████| 71/71 [00:15<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.18it/s]
                   all        324        324      0.386       0.35      0.308      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     60/100      2.56G      1.764      1.882      1.404          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.16it/s]
                   all        324        324      0.402      0.372      0.325      0.128
EarlyStopping: Training stopped early as no improvement observed in last 10 epochs. Best results observed at epoch 50, best model saved as best.pt.
To update EarlyStopping(patience=10) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.

60 epochs completed in 0.307 hours.
Optimizer stripped from runs/detect/train43/weights/last.pt, 5.3MB
Optimizer stripped from runs/detect/train43/weights/best.pt, 5.3MB

Validating runs/detect/train43/weights/best.pt...
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
YOLOv5n summary (fused): 84 layers, 2,503,334 parameters, 0 gradients, 7.1 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.25it/s]
                   all        324        324      0.477      0.388      0.353      0.135
                BENIGN        174        174      0.481      0.356      0.355      0.143
             MALIGNANT        150        150      0.473       0.42      0.351      0.126
Speed: 0.2ms preprocess, 2.5ms inference, 0.0ms loss, 2.0ms postprocess per image
Results saved to runs/detect/train43
Out[55]:
ultralytics.utils.metrics.DetMetrics object with attributes:

ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7fba8f058e20>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,
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           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,
           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,
           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,
            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,
           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,
           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,
           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,
            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,
           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,
           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,
           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,
            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,
           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,
           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,
           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,
           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,
           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,
           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,
           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,
           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,
           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,
           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,
           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,
           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.89655,     0.89655,     0.86782, ...,           0,           0,           0],
       [    0.88667,     0.88667,     0.82667, ...,           0,           0,           0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.15642811767360462)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([    0.14343,     0.12576])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.47680574727758174), 'metrics/recall(B)': np.float64(0.3881609195402299), 'metrics/mAP50(B)': np.float64(0.352938189824785), 'metrics/mAP50-95(B)': np.float64(0.13459366521236235), 'fitness': np.float64(0.15642811767360462)}
save_dir: PosixPath('runs/detect/train43')
speed: {'preprocess': 0.1829868544720941, 'inference': 2.477857427970495, 'loss': 0.006605914138533451, 'postprocess': 2.011933294987237}
task: 'detect'

YOLO v8 Paciencia 45¶

In [ ]:
model = YOLO('yolov8n.pt')
data_mass_path = os.path.join('final_dataset_yolo', 'data.yaml')
model.train(data=data_mass_path, epochs=120, patience = 45)
New https://pypi.org/project/ultralytics/8.3.127 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=final_dataset_yolo/data.yaml, epochs=120, time=None, patience=45, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train45, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train45
Overriding model.yaml nc=80 with nc=2

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    751702  ultralytics.nn.modules.head.Detect           [2, [64, 128, 256]]           
Model summary: 129 layers, 3,011,238 parameters, 3,011,222 gradients, 8.2 GFLOPs

Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 927.2±147.2 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 298.4±85.0 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train45/labels.jpg... 
In [4]:
df.head()
Out[4]:
epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
0 1 31.7220 2.34783 4.97834 1.71379 0.00115 0.34609 0.03220 0.01191 2.08884 3.75067 1.58988 0.000548 0.000548 0.000548
1 2 49.8062 2.16216 3.86718 1.59476 0.16575 0.19092 0.09857 0.03164 2.25879 3.39720 1.69511 0.001094 0.001094 0.001094
2 3 67.4871 2.21181 3.39654 1.62246 0.23572 0.26977 0.15420 0.05925 2.12477 2.81986 1.68035 0.001632 0.001632 0.001632
3 4 85.3997 2.17201 3.04748 1.59152 0.22115 0.24264 0.15074 0.06051 2.07679 2.78117 1.71249 0.001626 0.001626 0.001626
4 5 103.0380 2.16188 2.92345 1.63255 0.29474 0.29575 0.15845 0.06215 2.01306 2.65899 1.60202 0.001612 0.001612 0.001612

YOLO V11 paciencia 45¶

In [8]:
model_11 = YOLO("yolo11n.pt")
data_mass_path = os.path.join('final_dataset_yolo', 'data.yaml')
model_11.train(data=data_mass_path, epochs=120, patience = 45)
New https://pypi.org/project/ultralytics/8.3.127 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
engine/trainer: task=detect, mode=train, model=yolo11n.pt, data=final_dataset_yolo/data.yaml, epochs=120, time=None, patience=45, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train53, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train53
Overriding model.yaml nc=80 with nc=2

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      
  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     
  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           
 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          
 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           
 23        [16, 19, 22]  1    431062  ultralytics.nn.modules.head.Detect           [2, [64, 128, 256]]           
YOLO11n summary: 181 layers, 2,590,230 parameters, 2,590,214 gradients, 6.4 GFLOPs

Transferred 448/499 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 952.6±260.2 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 408.7±449.0 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train53/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/train53
Starting training for 120 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      1/120      2.48G      2.357       5.12      1.706          6        640: 100%|██████████| 71/71 [00:19<00:00,  3.62it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.36it/s]
                   all        324        324      0.486     0.0157     0.0921     0.0268

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      2/120      2.75G      2.196      4.018      1.607          4        640: 100%|██████████| 71/71 [00:18<00:00,  3.92it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.36it/s]
                   all        324        324      0.155      0.192     0.0823     0.0263

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      3/120      2.77G      2.214      3.529      1.644          8        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.55it/s]
                   all        324        324      0.128      0.138     0.0715     0.0271

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      4/120      2.78G      2.171      3.125      1.621          8        640: 100%|██████████| 71/71 [00:17<00:00,  4.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.47it/s]
                   all        324        324      0.227      0.174        0.1     0.0356

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      5/120      2.79G      2.198      2.976      1.629          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.05it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.66it/s]
                   all        324        324      0.202      0.251       0.14      0.052

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      6/120      2.81G      2.123      2.812      1.604          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.79it/s]
                   all        324        324      0.265      0.271      0.167     0.0657

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      7/120      2.81G      2.123      2.722      1.605          3        640: 100%|██████████| 71/71 [00:17<00:00,  4.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.86it/s]
                   all        324        324      0.271      0.242      0.137     0.0552

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      8/120      2.81G      2.106      2.733      1.636          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.69it/s]
                   all        324        324      0.212      0.263      0.129     0.0533

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      9/120      2.81G      2.052      2.608      1.578          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.72it/s]
                   all        324        324      0.283      0.342      0.204     0.0787

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     10/120      2.81G      2.085      2.636        1.6          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.58it/s]
                   all        324        324      0.359      0.307       0.22     0.0873

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     11/120      2.81G      2.078      2.557      1.578          3        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.86it/s]
                   all        324        324      0.283      0.337      0.207     0.0851

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     12/120      2.83G      2.041      2.578      1.581          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.59it/s]
                   all        324        324      0.303      0.296      0.216     0.0856

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     13/120      2.83G      2.054      2.612      1.576          4        640: 100%|██████████| 71/71 [00:18<00:00,  3.86it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.60it/s]
                   all        324        324      0.313      0.313      0.241      0.104

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     14/120      2.83G      2.055      2.542      1.603          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.96it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.79it/s]
                   all        324        324      0.351      0.329      0.253      0.101

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     15/120      2.83G      2.058      2.502      1.601          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.64it/s]
                   all        324        324      0.282      0.313      0.242     0.0957

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     16/120      2.83G      2.028       2.48      1.563          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324      0.337      0.304      0.242     0.0986

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     17/120      2.83G      2.066      2.538      1.567          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.62it/s]
                   all        324        324      0.306        0.3       0.19     0.0778

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     18/120      2.83G      2.002       2.48       1.57          9        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.87it/s]
                   all        324        324      0.406      0.308      0.279      0.112

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     19/120      2.83G      1.974      2.481       1.53          8        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.76it/s]
                   all        324        324      0.345      0.372       0.26      0.105

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     20/120      2.83G      1.988      2.405      1.523          4        640: 100%|██████████| 71/71 [00:17<00:00,  3.96it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.80it/s]
                   all        324        324      0.375      0.382      0.294      0.121

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     21/120      2.83G      2.025       2.47      1.571          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.66it/s]
                   all        324        324      0.279      0.323      0.235      0.094

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     22/120      2.83G      2.006       2.33      1.539          4        640: 100%|██████████| 71/71 [00:18<00:00,  3.94it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324       0.37      0.341      0.284      0.108

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     23/120      2.83G      2.011       2.43      1.565          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.56it/s]
                   all        324        324      0.267       0.24      0.164     0.0661

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     24/120      2.83G      1.976      2.457      1.583          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.67it/s]
                   all        324        324      0.379      0.297      0.243        0.1

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     25/120      2.83G      1.938      2.363      1.512          9        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.84it/s]
                   all        324        324       0.35      0.359      0.271       0.11

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     26/120      2.83G      1.931       2.34      1.502         10        640: 100%|██████████| 71/71 [00:17<00:00,  3.96it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324      0.324      0.352      0.277      0.115

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     27/120      2.83G      1.962      2.368      1.513          8        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.84it/s]
                   all        324        324      0.365      0.285      0.286      0.119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     28/120      2.83G      1.948      2.356      1.535          3        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.85it/s]
                   all        324        324      0.288      0.202      0.206     0.0762

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     29/120      2.83G      1.944      2.345      1.537          9        640: 100%|██████████| 71/71 [00:17<00:00,  3.96it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.81it/s]
                   all        324        324      0.364      0.365      0.283      0.111

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     30/120      2.83G      1.943      2.357      1.527         10        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324      0.357      0.351      0.302      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     31/120      2.83G      1.951      2.291      1.527          7        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.87it/s]
                   all        324        324      0.325      0.346      0.284       0.11

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     32/120      2.84G      1.922       2.28      1.479          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.80it/s]
                   all        324        324      0.393      0.311      0.279      0.117

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     33/120      2.84G      1.907      2.194      1.502          3        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324      0.429      0.359        0.3       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     34/120      2.84G      1.939      2.319      1.511          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324      0.365      0.361      0.275       0.11

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     35/120      2.84G      1.905      2.215      1.519          4        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.62it/s]
                   all        324        324      0.346      0.377      0.304       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     36/120      2.84G      1.939      2.243      1.525          8        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.73it/s]
                   all        324        324       0.34      0.308      0.244        0.1

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     37/120      2.84G      1.885      2.259      1.483          8        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.43it/s]
                   all        324        324      0.313      0.446      0.311      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     38/120      2.84G      1.956      2.282      1.525          7        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324      0.386      0.369      0.293      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     39/120      2.84G      1.884      2.232      1.474          7        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.37it/s]
                   all        324        324      0.416      0.321      0.306      0.123

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     40/120      2.84G      1.949      2.226       1.52          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.72it/s]
                   all        324        324      0.393      0.394      0.328      0.133

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     41/120      2.84G      1.904      2.152      1.498          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.86it/s]
                   all        324        324      0.376      0.362      0.316       0.13

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     42/120      2.84G       1.94       2.22      1.521          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.84it/s]
                   all        324        324      0.374      0.356      0.311      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     43/120      2.84G      1.888      2.211      1.474          9        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.68it/s]
                   all        324        324      0.406      0.384      0.314       0.13

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     44/120      2.84G      1.897       2.19      1.485          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.81it/s]
                   all        324        324      0.308       0.34      0.219     0.0863

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     45/120      2.84G      1.892      2.131      1.492          3        640: 100%|██████████| 71/71 [00:18<00:00,  3.90it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.72it/s]
                   all        324        324       0.35      0.451      0.327      0.137

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     46/120      2.84G       1.82      2.137      1.465          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.57it/s]
                   all        324        324       0.46      0.342      0.316      0.134

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     47/120      2.84G      1.844      2.072      1.447          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.71it/s]
                   all        324        324      0.394       0.36        0.3      0.117

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     48/120      2.84G      1.853      2.095      1.493          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.71it/s]
                   all        324        324      0.324      0.364      0.305      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     49/120      2.84G      1.807      2.122      1.451          3        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324      0.409      0.385      0.321      0.132

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     50/120      2.84G      1.838      2.091      1.467          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.70it/s]
                   all        324        324      0.391      0.417      0.322      0.134

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     51/120      2.84G      1.817      2.109      1.453          5        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.80it/s]
                   all        324        324       0.37       0.38      0.303      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     52/120      2.84G      1.827       2.07      1.434          7        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.81it/s]
                   all        324        324      0.331       0.44      0.327       0.13

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     53/120      2.84G      1.851      2.068      1.475          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.04it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.75it/s]
                   all        324        324       0.36      0.413      0.335      0.133

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     54/120      2.84G      1.835      2.106      1.471          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.46it/s]
                   all        324        324      0.375      0.377      0.313       0.13

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     55/120      2.84G       1.79      1.993       1.42          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.76it/s]
                   all        324        324      0.359      0.397      0.316      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     56/120      2.84G      1.806      2.016      1.445          7        640: 100%|██████████| 71/71 [00:17<00:00,  4.02it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.84it/s]
                   all        324        324       0.36      0.373      0.299       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     57/120      2.84G      1.824      2.073      1.447          4        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.84it/s]
                   all        324        324      0.358      0.379      0.309      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     58/120      2.84G      1.803      1.974      1.442          6        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.79it/s]
                   all        324        324       0.42      0.439      0.351      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     59/120      2.84G      1.811      2.006      1.466          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.03it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.86it/s]
                   all        324        324      0.385      0.414      0.319      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     60/120      2.84G       1.81      1.959      1.442          4        640: 100%|██████████| 71/71 [00:17<00:00,  3.97it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.77it/s]
                   all        324        324      0.408      0.367      0.314      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     61/120      2.84G      1.804      2.032      1.455          8        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.68it/s]
                   all        324        324      0.484       0.38      0.344      0.141

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     62/120      2.84G      1.799      1.983      1.425          9        640: 100%|██████████| 71/71 [00:17<00:00,  3.99it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.76it/s]
                   all        324        324      0.449      0.384      0.351      0.146

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     63/120      2.84G        1.8      1.984      1.447          4        640: 100%|██████████| 71/71 [00:17<00:00,  4.00it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.73it/s]
                   all        324        324       0.39      0.389      0.306      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     64/120      2.84G      1.754       1.92      1.407          3        640: 100%|██████████| 71/71 [00:17<00:00,  4.01it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.61it/s]
                   all        324        324      0.385      0.376      0.322      0.134

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     65/120      2.84G      1.748      1.953      1.439          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.95it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.90it/s]
                   all        324        324      0.429       0.38      0.346      0.145

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     66/120      2.84G      1.766      1.945      1.417          3        640: 100%|██████████| 71/71 [00:18<00:00,  3.94it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.84it/s]
                   all        324        324      0.432      0.382      0.328       0.13

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     67/120      2.84G      1.781      1.945      1.427         10        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.90it/s]
                   all        324        324      0.358      0.366      0.278      0.113

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     68/120      2.84G      1.724      1.893      1.395          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.98it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.66it/s]
                   all        324        324      0.384      0.417      0.325      0.133

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     69/120      2.84G       1.75      1.939      1.403          5        640: 100%|██████████| 71/71 [00:17<00:00,  3.97it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.77it/s]
                   all        324        324      0.463      0.392      0.352      0.141

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     70/120      2.84G      1.736      1.908      1.418          6        640: 100%|██████████| 71/71 [00:19<00:00,  3.70it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.49it/s]
                   all        324        324      0.411      0.437      0.357      0.144

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     71/120      2.84G      1.763      1.977      1.433          3        640: 100%|██████████| 71/71 [00:18<00:00,  3.83it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.86it/s]
                   all        324        324      0.406       0.39      0.333      0.134

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     72/120      2.84G      1.724      1.882      1.395          7        640: 100%|██████████| 71/71 [00:20<00:00,  3.50it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.80it/s]
                   all        324        324      0.391      0.374       0.31      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     73/120      2.84G      1.729      1.893      1.389          7        640: 100%|██████████| 71/71 [00:18<00:00,  3.89it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.86it/s]
                   all        324        324      0.406      0.374      0.329      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     74/120      2.84G      1.731      1.845      1.404          4        640: 100%|██████████| 71/71 [00:18<00:00,  3.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.31it/s]
                   all        324        324      0.407      0.386      0.309      0.117

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     75/120      2.84G      1.705      1.874      1.385          5        640: 100%|██████████| 71/71 [00:18<00:00,  3.86it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.88it/s]
                   all        324        324       0.39      0.423      0.334      0.133

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     76/120      2.84G      1.696      1.843      1.386          5        640: 100%|██████████| 71/71 [00:20<00:00,  3.51it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.49it/s]
                   all        324        324       0.44      0.414      0.328      0.138

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     77/120      2.84G      1.714      1.898      1.386          5        640: 100%|██████████| 71/71 [00:19<00:00,  3.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.72it/s]
                   all        324        324      0.466      0.365      0.342       0.14

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     78/120      2.84G      1.697      1.829      1.389          9        640: 100%|██████████| 71/71 [00:19<00:00,  3.61it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.67it/s]
                   all        324        324      0.421      0.352      0.321      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     79/120      2.84G      1.704       1.79      1.391          3        640: 100%|██████████| 71/71 [00:18<00:00,  3.81it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.73it/s]
                   all        324        324        0.4      0.368      0.319      0.127

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     80/120      2.84G      1.705      1.809      1.379          8        640: 100%|██████████| 71/71 [00:18<00:00,  3.83it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.83it/s]
                   all        324        324      0.451      0.375      0.337      0.132

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     81/120      2.84G      1.675       1.79      1.386          4        640: 100%|██████████| 71/71 [00:18<00:00,  3.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  2.98it/s]
                   all        324        324      0.446       0.37      0.338      0.133

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     82/120      2.84G      1.671       1.78       1.35          2        640: 100%|██████████| 71/71 [00:18<00:00,  3.85it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.51it/s]
                   all        324        324      0.433      0.372      0.326      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     83/120      2.84G      1.657       1.79      1.362          6        640: 100%|██████████| 71/71 [00:19<00:00,  3.66it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]
                   all        324        324        0.4      0.449      0.337      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     84/120      2.84G      1.675      1.769      1.357          9        640: 100%|██████████| 71/71 [00:19<00:00,  3.68it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324      0.419      0.364      0.334      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     85/120      2.84G      1.645      1.737      1.344          9        640: 100%|██████████| 71/71 [00:18<00:00,  3.82it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.52it/s]
                   all        324        324      0.397      0.378      0.314      0.129

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     86/120      2.84G      1.665      1.743      1.365          3        640: 100%|██████████| 71/71 [00:18<00:00,  3.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.73it/s]
                   all        324        324      0.428      0.415      0.344      0.141

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     87/120      2.84G      1.659      1.732      1.371          6        640: 100%|██████████| 71/71 [00:18<00:00,  3.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.73it/s]
                   all        324        324      0.407      0.415      0.344      0.143

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     88/120      2.84G      1.648      1.723      1.352          7        640: 100%|██████████| 71/71 [00:19<00:00,  3.67it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.56it/s]
                   all        324        324      0.367       0.41      0.311      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     89/120      2.84G      1.662      1.687      1.355          9        640: 100%|██████████| 71/71 [00:19<00:00,  3.56it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.47it/s]
                   all        324        324      0.473      0.381      0.327      0.132

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     90/120      2.84G      1.655      1.668      1.374          5        640: 100%|██████████| 71/71 [00:19<00:00,  3.71it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.81it/s]
                   all        324        324      0.442      0.393      0.338      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     91/120      2.84G      1.618      1.707       1.33          4        640: 100%|██████████| 71/71 [00:18<00:00,  3.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.36it/s]
                   all        324        324      0.416       0.41      0.352      0.141

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     92/120      2.84G      1.645      1.632      1.356          9        640: 100%|██████████| 71/71 [00:19<00:00,  3.65it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.12it/s]
                   all        324        324       0.39       0.38      0.317      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     93/120      2.84G      1.622      1.672      1.338          5        640: 100%|██████████| 71/71 [00:18<00:00,  3.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.79it/s]
                   all        324        324      0.413      0.355       0.31      0.127

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     94/120      2.84G      1.589      1.587      1.331          3        640: 100%|██████████| 71/71 [00:20<00:00,  3.40it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.73it/s]
                   all        324        324      0.395      0.435      0.328      0.129

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     95/120      2.84G      1.636      1.622      1.339          9        640: 100%|██████████| 71/71 [00:18<00:00,  3.85it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.55it/s]
                   all        324        324      0.451      0.391      0.344      0.132

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     96/120      2.84G        1.6      1.624      1.341          7        640: 100%|██████████| 71/71 [00:20<00:00,  3.49it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.54it/s]
                   all        324        324      0.446      0.357      0.335      0.137

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     97/120      2.84G      1.598      1.644       1.33          8        640: 100%|██████████| 71/71 [00:18<00:00,  3.79it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.27it/s]
                   all        324        324      0.395       0.44      0.343      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     98/120      2.84G      1.601      1.646      1.326          4        640: 100%|██████████| 71/71 [00:19<00:00,  3.57it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.78it/s]
                   all        324        324      0.395      0.404      0.321       0.13

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     99/120      2.84G      1.589      1.629      1.323          6        640: 100%|██████████| 71/71 [00:19<00:00,  3.56it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.44it/s]
                   all        324        324       0.42      0.418      0.339      0.137

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    100/120      2.84G      1.607      1.624      1.323         10        640: 100%|██████████| 71/71 [00:19<00:00,  3.56it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.53it/s]
                   all        324        324      0.399      0.405      0.341      0.141

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    101/120      2.84G       1.56        1.6      1.311          8        640: 100%|██████████| 71/71 [00:18<00:00,  3.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.65it/s]
                   all        324        324      0.444      0.438      0.356      0.141

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    102/120      2.84G      1.562      1.564      1.325          7        640: 100%|██████████| 71/71 [00:19<00:00,  3.64it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.49it/s]
                   all        324        324      0.416      0.445      0.348      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    103/120      2.84G       1.53      1.529      1.301          8        640: 100%|██████████| 71/71 [00:18<00:00,  3.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.61it/s]
                   all        324        324      0.432      0.391      0.342      0.139

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    104/120      2.84G      1.558      1.551      1.302          4        640: 100%|██████████| 71/71 [00:19<00:00,  3.57it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.02it/s]
                   all        324        324      0.507      0.308      0.334      0.137

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    105/120      2.84G      1.542      1.516      1.313          7        640: 100%|██████████| 71/71 [00:19<00:00,  3.58it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.46it/s]
                   all        324        324      0.406      0.384      0.332      0.137

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    106/120      2.84G      1.546      1.496      1.318          3        640: 100%|██████████| 71/71 [00:20<00:00,  3.48it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.65it/s]
                   all        324        324      0.426      0.366      0.341      0.133

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    107/120      2.84G      1.549      1.527      1.313          5        640: 100%|██████████| 71/71 [00:18<00:00,  3.86it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.43it/s]
                   all        324        324      0.393      0.407      0.341      0.138
EarlyStopping: Training stopped early as no improvement observed in last 45 epochs. Best results observed at epoch 62, best model saved as best.pt.
To update EarlyStopping(patience=45) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.

107 epochs completed in 0.655 hours.
Optimizer stripped from runs/detect/train53/weights/last.pt, 5.5MB
Optimizer stripped from runs/detect/train53/weights/best.pt, 5.5MB

Validating runs/detect/train53/weights/best.pt...
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
YOLO11n summary (fused): 100 layers, 2,582,542 parameters, 0 gradients, 6.3 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.53it/s]
                   all        324        324      0.444      0.393      0.353      0.147
                BENIGN        174        174      0.512      0.332      0.355      0.147
             MALIGNANT        150        150      0.377      0.453      0.351      0.146
Speed: 0.2ms preprocess, 3.1ms inference, 0.0ms loss, 3.3ms postprocess per image
Results saved to runs/detect/train53
Out[8]:
ultralytics.utils.metrics.DetMetrics object with attributes:

ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f7f8d0841c0>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,
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           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,
           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,
           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,
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           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,
           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,
           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,
           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,
           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,
           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,
           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,
           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,
           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,
           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,
           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,
           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.89655,     0.89655,     0.87356, ...,           0,           0,           0],
       [    0.90667,     0.90667,        0.88, ...,           0,           0,           0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.16718371543735006)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([     0.1473,     0.14576])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.44446430853375707), 'metrics/recall(B)': np.float64(0.3927243910577244), 'metrics/mAP50(B)': np.float64(0.3530777145213868), 'metrics/mAP50-95(B)': np.float64(0.14652882665023487), 'fitness': np.float64(0.16718371543735006)}
save_dir: PosixPath('runs/detect/train53')
speed: {'preprocess': 0.1796138915520759, 'inference': 3.140773724787581, 'loss': 0.0019342535071902804, 'postprocess': 3.310438906658947}
task: 'detect'

YOLO v5 paciencia 45¶

In [6]:
# Entrenamiento modelo mass YOLO v5

model_55 = YOLO("yolov5n.pt")
data_mass_path = os.path.join('final_dataset_yolo', 'data.yaml')
model_55.train(data=data_mass_path, epochs=120, patience = 45)
PRO TIP 💡 Replace 'model=yolov5n.pt' with new 'model=yolov5nu.pt'.
YOLOv5 'u' models are trained with https://github.com/ultralytics/ultralytics and feature improved performance vs standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.

New https://pypi.org/project/ultralytics/8.3.127 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
engine/trainer: task=detect, mode=train, model=yolov5n.pt, data=final_dataset_yolo/data.yaml, epochs=120, time=None, patience=45, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train51, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train51
Overriding model.yaml nc=80 with nc=2

                   from  n    params  module                                       arguments                     
  0                  -1  1      1760  ultralytics.nn.modules.conv.Conv             [3, 16, 6, 2, 2]              
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      4800  ultralytics.nn.modules.block.C3              [32, 32, 1]                   
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     29184  ultralytics.nn.modules.block.C3              [64, 64, 2]                   
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  3    156928  ultralytics.nn.modules.block.C3              [128, 128, 3]                 
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    296448  ultralytics.nn.modules.block.C3              [256, 256, 1]                 
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1     33024  ultralytics.nn.modules.conv.Conv             [256, 128, 1, 1]              
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  1     90880  ultralytics.nn.modules.block.C3              [256, 128, 1, False]          
 14                  -1  1      8320  ultralytics.nn.modules.conv.Conv             [128, 64, 1, 1]               
 15                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 16             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 17                  -1  1     22912  ultralytics.nn.modules.block.C3              [128, 64, 1, False]           
 18                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 19            [-1, 14]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 20                  -1  1     74496  ultralytics.nn.modules.block.C3              [128, 128, 1, False]          
 21                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 22            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 23                  -1  1    296448  ultralytics.nn.modules.block.C3              [256, 256, 1, False]          
 24        [17, 20, 23]  1    751702  ultralytics.nn.modules.head.Detect           [2, [64, 128, 256]]           
YOLOv5n summary: 153 layers, 2,508,854 parameters, 2,508,838 gradients, 7.2 GFLOPs

Transferred 391/427 items from pretrained weights
Freezing layer 'model.24.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 798.5±219.5 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 460.2±451.0 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train51/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 69 weight(decay=0.0), 76 weight(decay=0.0005), 75 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/train51
Starting training for 120 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      1/120      2.17G      2.345      5.011      1.756          6        640: 100%|██████████| 71/71 [00:17<00:00,  4.12it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.44it/s]
                   all        324        324    0.00167      0.499     0.0577     0.0169

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      2/120      2.44G      2.194      3.771      1.585          4        640: 100%|██████████| 71/71 [00:15<00:00,  4.60it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]
                   all        324        324      0.158      0.211     0.0951     0.0339

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      3/120      2.44G      2.201      3.322      1.645          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.81it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.94it/s]
                   all        324        324      0.181      0.339      0.141     0.0523

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      4/120      2.45G      2.131      3.042      1.623          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.248      0.269      0.149     0.0594

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      5/120      2.46G      2.168      2.882      1.625          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.00it/s]
                   all        324        324      0.195      0.221      0.108     0.0423

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      6/120      2.48G      2.107       2.79      1.616          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.04it/s]
                   all        324        324      0.223      0.271      0.171     0.0656

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      7/120      2.49G      2.094       2.73      1.627          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.277      0.199       0.13     0.0476

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      8/120      2.51G       2.11      2.714      1.637          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.90it/s]
                   all        324        324      0.236      0.136      0.107     0.0459

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      9/120      2.51G      2.059      2.619      1.576          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.79it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.01it/s]
                   all        324        324       0.28      0.336      0.207     0.0798

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     10/120      2.54G      2.093      2.662      1.587          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.91it/s]
                   all        324        324      0.283      0.285       0.19       0.08

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     11/120      2.54G      2.079      2.582      1.599          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.86it/s]
                   all        324        324      0.248      0.295      0.195     0.0752

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     12/120      2.54G      2.054      2.614      1.556          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.04it/s]
                   all        324        324      0.279      0.257      0.195     0.0781

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     13/120      2.54G      2.029      2.631      1.548          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.04it/s]
                   all        324        324      0.285      0.287        0.2     0.0906

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     14/120      2.54G      2.056      2.567      1.583          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.70it/s]
                   all        324        324      0.309      0.331      0.236     0.0906

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     15/120      2.54G       2.06      2.523        1.6          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.302      0.315      0.217     0.0799

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     16/120      2.54G      2.031      2.472       1.57          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.323       0.31      0.238     0.0992

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     17/120      2.54G       2.07      2.499      1.562          6        640: 100%|██████████| 71/71 [00:15<00:00,  4.63it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.16it/s]
                   all        324        324       0.34      0.282      0.232     0.0935

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     18/120      2.54G      2.023      2.516      1.592          9        640: 100%|██████████| 71/71 [00:15<00:00,  4.72it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.73it/s]
                   all        324        324       0.37      0.379      0.295      0.116

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     19/120      2.54G      1.991      2.465      1.568          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324      0.352      0.295      0.265      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     20/120      2.54G      2.006      2.408      1.538          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.06it/s]
                   all        324        324      0.379      0.345      0.276      0.114

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     21/120      2.54G      2.005      2.466      1.578          5        640: 100%|██████████| 71/71 [00:15<00:00,  4.71it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324      0.376      0.333      0.265        0.1

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     22/120      2.54G      2.003      2.343      1.553          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.376      0.352      0.272        0.1

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     23/120      2.54G      1.981      2.413      1.536          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.311      0.301      0.229      0.088

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     24/120      2.54G      1.976      2.459      1.564          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.07it/s]
                   all        324        324      0.379      0.363      0.276      0.111

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     25/120      2.54G       1.95      2.372      1.534          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.11it/s]
                   all        324        324      0.371      0.335      0.282      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     26/120      2.54G      1.938      2.326      1.517         10        640: 100%|██████████| 71/71 [00:15<00:00,  4.72it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.17it/s]
                   all        324        324      0.374      0.333      0.286      0.114

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     27/120      2.54G      1.966      2.339      1.525          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.336       0.36       0.27      0.108

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     28/120      2.54G      1.942       2.35      1.544          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324      0.377      0.308      0.256     0.0949

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     29/120      2.54G      1.939      2.326      1.541          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.01it/s]
                   all        324        324      0.322       0.42      0.276       0.11

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     30/120      2.54G      1.927      2.319      1.526         10        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.01it/s]
                   all        324        324      0.347      0.405      0.289      0.111

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     31/120      2.54G      1.944      2.271       1.55          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.79it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.395      0.341      0.293      0.121

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     32/120      2.54G      1.946      2.275        1.5          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.393       0.36      0.297      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     33/120      2.54G       1.91      2.217      1.506          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.79it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.10it/s]
                   all        324        324       0.39      0.394      0.296      0.116

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     34/120      2.54G      1.937      2.269      1.502          6        640: 100%|██████████| 71/71 [00:15<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.61it/s]
                   all        324        324      0.438      0.376      0.307       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     35/120      2.54G      1.905        2.2      1.516          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.355      0.368      0.288      0.116

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     36/120      2.54G      1.951      2.213      1.513          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.10it/s]
                   all        324        324      0.382      0.414      0.288       0.11

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     37/120      2.54G      1.884      2.217      1.473          8        640: 100%|██████████| 71/71 [00:15<00:00,  4.62it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.95it/s]
                   all        324        324      0.387      0.336      0.279      0.116

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     38/120      2.54G      1.961      2.273       1.53          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.93it/s]
                   all        324        324      0.341       0.34       0.26      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     39/120      2.54G      1.874      2.222      1.466          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.70it/s]
                   all        324        324      0.371      0.413      0.326      0.125

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     40/120      2.54G       1.93      2.229       1.51          6        640: 100%|██████████| 71/71 [00:15<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]
                   all        324        324       0.43      0.363      0.333      0.131

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     41/120      2.54G      1.879      2.155      1.478          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.10it/s]
                   all        324        324      0.386      0.332      0.294      0.117

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     42/120      2.54G      1.916      2.219      1.517          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.341      0.419      0.298       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     43/120      2.54G       1.84      2.183      1.451          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324      0.396      0.346      0.302      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     44/120      2.54G      1.871      2.173      1.475          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.349      0.402      0.288      0.108

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     45/120      2.54G      1.881      2.148       1.51          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.13it/s]
                   all        324        324      0.456      0.421      0.354      0.148

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     46/120      2.54G      1.836      2.113      1.476          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324       0.37      0.321      0.263      0.103

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     47/120      2.54G      1.863      2.047      1.469          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.90it/s]
                   all        324        324      0.367      0.372      0.301      0.116

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     48/120      2.54G      1.867       2.05      1.504          7        640: 100%|██████████| 71/71 [00:15<00:00,  4.72it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.13it/s]
                   all        324        324      0.409       0.38      0.311      0.129

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     49/120      2.54G      1.822        2.1      1.463          3        640: 100%|██████████| 71/71 [00:15<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.74it/s]
                   all        324        324       0.41      0.379      0.312      0.129

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     50/120      2.54G      1.837      2.099      1.476          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.321      0.389      0.262      0.102

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     51/120      2.54G      1.819      2.108       1.45          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.06it/s]
                   all        324        324      0.367      0.401      0.293      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     52/120      2.54G       1.84      2.025      1.455          7        640: 100%|██████████| 71/71 [00:15<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.92it/s]
                   all        324        324      0.429       0.41      0.334      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     53/120      2.54G       1.83      2.042      1.469          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324      0.394       0.41      0.299      0.123

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     54/120      2.54G      1.814      2.047      1.455          6        640: 100%|██████████| 71/71 [00:15<00:00,  4.72it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.93it/s]
                   all        324        324      0.416      0.398      0.333      0.132

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     55/120      2.54G      1.812      1.985      1.434          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.403      0.419       0.32      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     56/120      2.54G      1.783      1.963      1.446          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.98it/s]
                   all        324        324      0.436      0.421      0.347      0.141

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     57/120      2.54G      1.808      2.034      1.428          4        640: 100%|██████████| 71/71 [00:15<00:00,  4.70it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.20it/s]
                   all        324        324      0.421      0.396      0.307      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     58/120      2.54G      1.793      1.954      1.448          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.98it/s]
                   all        324        324      0.451      0.349      0.301      0.123

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     59/120      2.54G      1.763      1.953       1.44          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.94it/s]
                   all        324        324      0.403      0.412      0.349      0.138

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     60/120      2.54G      1.798      1.906      1.437          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.18it/s]
                   all        324        324      0.431      0.359      0.319       0.13

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     61/120      2.54G      1.807       1.97      1.468          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324      0.437      0.413       0.34      0.139

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     62/120      2.54G      1.803      1.936      1.445          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.21it/s]
                   all        324        324      0.396      0.445       0.35      0.141

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     63/120      2.54G       1.79      1.923      1.451          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.71it/s]
                   all        324        324      0.403      0.386      0.306      0.125

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     64/120      2.54G      1.747      1.877      1.404          3        640: 100%|██████████| 71/71 [00:15<00:00,  4.70it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.06it/s]
                   all        324        324      0.428      0.359      0.313      0.125

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     65/120      2.54G      1.758      1.893      1.435          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.13it/s]
                   all        324        324       0.39      0.403      0.316      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     66/120      2.54G      1.754       1.92      1.417          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.17it/s]
                   all        324        324      0.399      0.375      0.309      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     67/120      2.54G      1.742       1.87      1.411         10        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.02it/s]
                   all        324        324      0.407      0.367      0.308      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     68/120      2.54G      1.718      1.859      1.399          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.74it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.98it/s]
                   all        324        324      0.426      0.343       0.28      0.109

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     69/120      2.54G      1.734      1.871      1.388          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.16it/s]
                   all        324        324      0.398      0.396      0.316      0.126

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     70/120      2.54G      1.725      1.861      1.404          6        640: 100%|██████████| 71/71 [00:15<00:00,  4.66it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.12it/s]
                   all        324        324      0.425      0.361      0.328      0.135

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     71/120      2.54G      1.714      1.909      1.403          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.00it/s]
                   all        324        324      0.399      0.375      0.288      0.119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     72/120      2.54G      1.704      1.785       1.39          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.03it/s]
                   all        324        324       0.37      0.395      0.292      0.113

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     73/120      2.54G      1.684      1.808      1.369          7        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.396       0.38      0.293      0.117

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     74/120      2.54G      1.683      1.754      1.383          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]
                   all        324        324      0.388       0.43      0.312      0.125

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     75/120      2.54G      1.674      1.804      1.362          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]
                   all        324        324      0.335      0.431      0.316      0.127

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     76/120      2.54G      1.673      1.745      1.378          5        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.07it/s]
                   all        324        324      0.393      0.363      0.287      0.116

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     77/120      2.54G       1.67      1.796      1.354          5        640: 100%|██████████| 71/71 [00:15<00:00,  4.72it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.21it/s]
                   all        324        324       0.42      0.348      0.313      0.133

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     78/120      2.54G      1.657      1.747      1.361          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.79it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.457      0.338      0.302      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     79/120      2.54G      1.666      1.722      1.376          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.79it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.09it/s]
                   all        324        324      0.406      0.407       0.32      0.125

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     80/120      2.54G      1.656      1.725      1.353          8        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.87it/s]
                   all        324        324      0.468      0.337       0.32      0.128

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     81/120      2.54G      1.663      1.709      1.364          4        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324      0.388        0.4      0.317       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     82/120      2.54G      1.648      1.672      1.351          2        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.04it/s]
                   all        324        324      0.393        0.4       0.31       0.12

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     83/120      2.54G      1.621      1.696      1.344          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.19it/s]
                   all        324        324      0.368      0.384      0.301      0.117

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     84/120      2.54G      1.645      1.664      1.333          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.78it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.01it/s]
                   all        324        324      0.411       0.41      0.326      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     85/120      2.54G      1.616      1.645      1.337          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.80it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]
                   all        324        324      0.409       0.42      0.323      0.123

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     86/120      2.54G      1.625      1.671      1.353          3        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.13it/s]
                   all        324        324      0.448      0.359      0.313      0.118

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     87/120      2.54G      1.606      1.628      1.342          6        640: 100%|██████████| 71/71 [00:14<00:00,  4.76it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.20it/s]
                   all        324        324      0.389      0.386      0.309      0.124

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     88/120      2.54G      1.595      1.612      1.326          7        640: 100%|██████████| 71/71 [00:15<00:00,  4.73it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]
                   all        324        324      0.401      0.394      0.313      0.122

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     89/120      2.54G      1.611       1.58      1.332          9        640: 100%|██████████| 71/71 [00:14<00:00,  4.77it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.06it/s]
                   all        324        324      0.373      0.402      0.307      0.119

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
     90/120      2.54G      1.595      1.553      1.347          5        640: 100%|██████████| 71/71 [00:15<00:00,  4.65it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.22it/s]
                   all        324        324      0.395      0.371      0.291      0.111
EarlyStopping: Training stopped early as no improvement observed in last 45 epochs. Best results observed at epoch 45, best model saved as best.pt.
To update EarlyStopping(patience=45) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.

90 epochs completed in 0.458 hours.
Optimizer stripped from runs/detect/train51/weights/last.pt, 5.3MB
Optimizer stripped from runs/detect/train51/weights/best.pt, 5.3MB

Validating runs/detect/train51/weights/best.pt...
Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB)
YOLOv5n summary (fused): 84 layers, 2,503,334 parameters, 0 gradients, 7.1 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.26it/s]
                   all        324        324      0.458      0.421      0.353      0.148
                BENIGN        174        174      0.424      0.443      0.363      0.158
             MALIGNANT        150        150      0.492        0.4      0.344      0.137
Speed: 0.2ms preprocess, 2.6ms inference, 0.0ms loss, 2.0ms postprocess per image
Results saved to runs/detect/train51
Out[6]:
ultralytics.utils.metrics.DetMetrics object with attributes:

ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f7f8d136080>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,
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       [       0.84,        0.84,     0.79333, ...,           0,           0,           0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.16818767233950643)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([    0.15841,     0.13679])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.457845233635291), 'metrics/recall(B)': np.float64(0.421264367816092), 'metrics/mAP50(B)': np.float64(0.3534774544660081), 'metrics/mAP50-95(B)': np.float64(0.14759991876989514), 'fitness': np.float64(0.16818767233950643)}
save_dir: PosixPath('runs/detect/train51')
speed: {'preprocess': 0.20119547556487866, 'inference': 2.585076048602293, 'loss': 0.0007237727397385939, 'postprocess': 2.0048052164507504}
task: 'detect'

Resultados YOLOv8 paciencia 10¶

In [1]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Ruta a tu carpeta de resultados
ruta = 'runs/detect/train40/'

# Lista de imágenes 
imagenes = ['val_batch1_pred.jpg', 'val_batch1_labels.jpg', 'P_curve.png', 'val_batch2_pred.jpg',
'labels.jpg', 'R_curve.png', 'PR_curve.png','val_batch0_pred.jpg', 'train_batch2.jpg', 'results.png',
'confusion_matrix_normalized.png', 'labels_correlogram.jpg','F1_curve.png', 'val_batch2_labels.jpg', 'train_batch1.jpg','confusion_matrix.png', 'val_batch0_labels.jpg', 'train_batch0.jpg']

for img in imagenes:
    try:
        img_path = ruta + img
        image = mpimg.imread(img_path)
        plt.figure(figsize=(10, 6))
        plt.imshow(image)
        plt.axis('off')
        plt.title(img)
        plt.show()
    except FileNotFoundError:
        print(f"{img} no encontrado.")
In [2]:
import pandas as pd

# Cargamos los resultados
df = pd.read_csv("runs/detect/train40/results.csv")

# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]

# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
    epoch  metrics/precision(B)  metrics/recall(B)  metrics/mAP50(B)
27     28               0.39796            0.40287           0.32845

Resultados YOLO V11 paciencia 10¶

In [3]:
ruta = 'runs/detect/train41/'
for img in imagenes:
    try:
        img_path = ruta + img
        image = mpimg.imread(img_path)
        plt.figure(figsize=(10, 6))
        plt.imshow(image)
        plt.axis('off')
        plt.title(img)
        plt.show()
    except FileNotFoundError:
        print(f"{img} no encontrado.")
In [4]:
import pandas as pd

# Cargamos los resultados
df = pd.read_csv("runs/detect/train41/results.csv")

# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]

# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
    epoch  metrics/precision(B)  metrics/recall(B)  metrics/mAP50(B)
44     45                 0.401            0.38379           0.34571

Resultados YOLO v5 con paciencia 10¶

In [5]:
ruta = 'runs/detect/train43/'

for img in imagenes:
    try:
        img_path = ruta + img
        image = mpimg.imread(img_path)
        plt.figure(figsize=(10, 6))
        plt.imshow(image)
        plt.axis('off')
        plt.title(img)
        plt.show()
    except FileNotFoundError:
        print(f"{img} no encontrado.")
In [6]:
import pandas as pd

# Cargamos los resultados
df = pd.read_csv("runs/detect/train43/results.csv")

# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]

# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
    epoch  metrics/precision(B)  metrics/recall(B)  metrics/mAP50(B)
49     50               0.47706            0.38816           0.35294

Resultados YOLO v8 PACIENCIA 45¶

In [7]:
import matplotlib.image as mpimg
ruta = 'runs/detect/train45/'
imagenes = ['val_batch1_pred.jpg', 'val_batch1_labels.jpg', 'P_curve.png', 'val_batch2_pred.jpg',
'labels.jpg', 'R_curve.png', 'PR_curve.png','val_batch0_pred.jpg', 'train_batch2.jpg', 'results.png',
'confusion_matrix_normalized.png', 'labels_correlogram.jpg','F1_curve.png', 'val_batch2_labels.jpg', 'train_batch1.jpg','confusion_matrix.png', 'val_batch0_labels.jpg', 'train_batch0.jpg']

for img in imagenes:
    try:
        img_path = ruta + img
        image = mpimg.imread(img_path)
        plt.figure(figsize=(10, 6))
        plt.imshow(image)
        plt.axis('off')
        plt.title(img)
        plt.show()
    except FileNotFoundError:
        print(f"{img} no encontrado.")
In [8]:
import pandas as pd

# Cargamos los resultados
df = pd.read_csv("runs/detect/train45/results.csv")

# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]

# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
    epoch  metrics/precision(B)  metrics/recall(B)  metrics/mAP50(B)
39     40               0.51028             0.3946           0.35993

Resultados YOLO V11 paciencia 45¶

In [9]:
ruta = 'runs/detect/train53/'

for img in imagenes:
    try:
        img_path = ruta + img
        image = mpimg.imread(img_path)
        plt.figure(figsize=(10, 6))
        plt.imshow(image)
        plt.axis('off')
        plt.title(img)
        plt.show()
    except FileNotFoundError:
        print(f"{img} no encontrado.")
In [10]:
import pandas as pd

# Cargamos los resultados
df = pd.read_csv("runs/detect/train53/results.csv")

# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]

# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
    epoch  metrics/precision(B)  metrics/recall(B)  metrics/mAP50(B)
69     70               0.41051             0.4369           0.35693

Resultados YOLO v5 paciencia 45¶

In [11]:
ruta = 'runs/detect/train51/'

for img in imagenes:
    try:
        img_path = ruta + img
        image = mpimg.imread(img_path)
        plt.figure(figsize=(10, 6))
        plt.imshow(image)
        plt.axis('off')
        plt.title(img)
        plt.show()
    except FileNotFoundError:
        print(f"{img} no encontrado.")
In [12]:
import pandas as pd

# Cargamos los resultados
df = pd.read_csv("runs/detect/train51/results.csv")

# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]

# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
    epoch  metrics/precision(B)  metrics/recall(B)  metrics/mAP50(B)
44     45               0.45629            0.42126            0.3537
In [ ]: